question string | paper_id string | chunk_index int32 | chunk string | label int32 | answerable bool | spans dict | source string | retrieval_rank int32 | gold_paper string | gold_chunk int32 | predicted_texts list | latency_s float32 | err string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
skipgram model character n-grams word representations | P16-2023 | 7 | ## 6 Results
The results are reported in terms of perplexity, in Table 1 for English, and in Table 2 for FlemishDutch. We computed baseline perplexity scores with SRILM (Stolcke, 2002) for 1bw. We used an interpolated modified Kneser-Ney language model, with Good-Turing discounting to mimic our thresholding options. A... | 1 | true | {
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"Upon inspection of the model sizes, we observe that the skipgram model contains almost five times as many parameters as the n -gram model. This difference is explained by the addition of skipgrams of length 3 and 4, and the bigrams and unigrams deri... | retrieved | 1 | Q17-1010 | 17 | [
"Upon inspection of the model sizes, we observe that the skipgram model contains almost five times as many parameters as the n -gram model. This difference is explained by the addition of skipgrams of length 3 and 4, and the bigrams and unigrams derived from these skipgrams. Each 4gram can be deconstructed into thr... | 3.496642 | |
skipgram model character n-grams word representations | 2021.ranlp-1.34 | 10 | ## 4.2 FastText Based Model
FastText 7 is an open-source library, developed by Facebook AI Research lab with the purpose of text classification and representation. As Bojanowski et al. (2016) described in their work, fastText creates word representations based on the skipgram model, where each word is represented as a... | 1 | true | {
"start": [
165
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"end": [
1004
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"text": [
"As Bojanowski et al. (2016) described in their work, fastText creates word representations based on the skipgram model, where each word is represented as a bag of character n-grams. A vector representation is associated to each character n-gram, word... | retrieved | 2 | Q17-1010 | 17 | [
"As Bojanowski et al. (2016) described in their work, fastText creates word representations based on the skipgram model, where each word is represented as a bag of character n-grams. A vector representation is associated to each character n-gram, words being represented as the sum of these representations. Using ch... | 3.496642 | |
skipgram model character n-grams word representations | P16-2023 | 6 | ## 5 Experimental Setup
We train 4-gram language model on the two training corpora, the Google 1 billion word benchmark and the Mediargus corpus. We do not perform any preprocessing on the data except tokenisation. The models are trained with a HPYLM. We do not use sentence beginning and end markers. The results for t... | 0 | false | {
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} | retrieved | 3 | Q17-1010 | 17 | [] | 3.496642 | |
What is the Revision module? | W19-4502 | 21 | ## 5.8 Module: Revision
While the previous modules handle major tasks, a processed locution may still need additional adjustments, including grammar correction. Hence, the Revision module makes adjustments to a processed locution and outputs proposition(s). This task is formulated as a seq2seq problem, i.e., a model a... | 1 | true | {
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25
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"end": [
395
],
"text": [
"While the previous modules handle major tasks, a processed locution may still need additional adjustments, including grammar correction. Hence, the Revision module makes adjustments to a processed locution and outputs proposition(s). This task is formu... | gold | -1 | W19-4502 | 21 | [
"While the previous modules handle major tasks, a processed locution may still need additional adjustments, including grammar correction. Hence, the Revision module makes adjustments to a processed locution and outputs proposition(s). This task is formulated as a seq2seq problem, i.e., a model automatically learns ... | 2.198435 | |
What is the Revision module? | 2020.lrec-1.111 | 8 | ## 4.2.3 Module 3: Revision Detector
It is mainly a manuscript viewer, which can be used directly after pre-processing the data. It will highlight three different kinds of revisions in manuscripts. Crossed out areas, annotations made above a text line, and probable changes of single letters (e.g. if a scribe changes t... | 1 | true | {
"start": [
38
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"end": [
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"text": [
"It is mainly a manuscript viewer, which can be used directly after pre-processing the data. It will highlight three different kinds of revisions in manuscripts. Crossed out areas, annotations made above a text line, and probable changes of single lette... | retrieved | 1 | W19-4502 | 21 | [
"It is mainly a manuscript viewer, which can be used directly after pre-processing the data. It will highlight three different kinds of revisions in manuscripts. Crossed out areas, annotations made above a text line, and probable changes of single letters (e.g. if a scribe changes the letter <a> to <e>). The model ... | 2.198435 | |
What is the Revision module? | P11-4017 | 4 | ## 4 Efficient Access to Revisions
Even though article revisions are available from the official Wikipedia revision dumps, accessing this information on a large scale is still a difficult task. This is due to two main problems. First, the revision dump contains all revisions as full text. This results in a massive amo... | 1 | true | {
"start": [
473
],
"end": [
555
],
"text": [
"Thus, we have developed a tool called RevisionMachine , which solves these issues."
]
} | retrieved | 2 | W19-4502 | 21 | [
"Thus, we have developed a tool called RevisionMachine , which solves these issues."
] | 2.198435 | |
What is the Revision module? | P11-4017 | 9 | ## Acknowledgments
This work has been supported by the Volkswagen Foundation as part of the Lichtenberg-Professorship Program under grant No. I/82806, and by the Hessian research excellence program 'Landes-Offensive zur Entwicklung Wissenschaftlich-¨ okonomischer Exzellenz' ( LOEWE ) as part of the research center 'Di... | 0 | false | {
"start": [],
"end": [],
"text": []
} | retrieved | 5 | W19-4502 | 21 | [] | 2.198435 | |
mental health models features | 2021.clpsych-1.19 | 5 | ## 4.1 Topic Model Analysis
Model. We use a topic model analysis to identify topic distribution differences between demographic groups. We train on each dataset (separately) a Partially Labeled LDA model (Ramage et al., 2011), which incorporates per-label latent topics into an LDA model. We assign both depression and ... | 1 | true | {
"start": [
36
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"end": [
679
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"text": [
"We use a topic model analysis to identify topic distribution differences between demographic groups. We train on each dataset (separately) a Partially Labeled LDA model (Ramage et al., 2011), which incorporates per-label latent topics into an LDA model... | gold | -1 | 2021.clpsych-1.19 | 5 | [
"We use a topic model analysis to identify topic distribution differences between demographic groups. We train on each dataset (separately) a Partially Labeled LDA model (Ramage et al., 2011), which incorporates per-label latent topics into an LDA model. We assign both depression and demographic labels to individua... | 6.166574 | |
mental health models features | 2025.c3nlp-1.10 | 1 | ## 1 Introduction
Over 197 million individuals in India are diagnosed with mental health disorders (Sagar et al., 2020), a disproportionate majority of whom do not receive mental healthcare (Singh, 2018). Generative AI technologies can facilitate affordable and easily accessible mental health assessment and support, e... | 1 | true | {
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1815
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2641
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"To inform the research on culturally competent mental health models (Sue, 1998), we adopt interpretable features that are comprehensible to stakeholders such as psychologists and policymakers, for modeling cross-cultural variations in mental health ... | retrieved | 3 | 2021.clpsych-1.19 | 5 | [
"To inform the research on culturally competent mental health models (Sue, 1998), we adopt interpretable features that are comprehensible to stakeholders such as psychologists and policymakers, for modeling cross-cultural variations in mental health language. We use psychosocial word categories (e.g., Linguistic In... | 6.166574 | |
VideoCLIP video text transformer properties | 2021.emnlp-main.544 | 19 | ## 6 Conclusion
We have presented VideoCLIP, an approach to pretrain a video-text model for zero-shot transfer to end tasks that require fine-grained association between video and language. VideoCLIP uses an objective that contrasts temporally overlapping positives with hard negatives stemming from nearest neighbor re... | 0 | false | {
"start": [],
"end": [],
"text": []
} | retrieved | 2 | 2021.emnlp-main.544 | 5 | [] | 3.256081 | |
VideoCLIP video text transformer properties | 2021.emnlp-main.544 | 4 | ## 3 VideoCLIP Pre-training
In the paradigm of multi-modal video-text pretraining for zero-shot transfer, the key challenge is to learn fine-grained association in-between video and text to cover the diverse needs of end tasks. We cover VideoCLIP pre-training in this section, and discuss the needs of zero-shot transfe... | 0 | false | {
"start": [],
"end": [],
"text": []
} | retrieved | 3 | 2021.emnlp-main.544 | 5 | [] | 3.256081 | |
12 documents annotated signal instances | W19-2708 | 7 | ## 3.2 The Signal Annotation System
Prior to this work, rstWeb had no support for signal annotation. The contribution of the present work was to build a signal annotation system on top of rstWeb to allow annotators to view and edit signal annotations and make these available for export and use in downstream tasks.
In... | 0 | false | {
"start": [],
"end": [],
"text": []
} | retrieved | 1 | W19-2710 | 7 | [] | 1.585181 | |
12 documents annotated signal instances | W19-2708 | 8 | ## 3.3 Data Model
A signal in our system consists of four elements:
1. A relation whose type (RESULT, CONCESSION, etc.) the signal is helping to indicate
2. A possibly empty list of tokens which comprise the signal
3. A type that categorizes the signal according to its linguistic nature
4. A more fine-grained subtype... | 0 | false | {
"start": [],
"end": [],
"text": []
} | retrieved | 3 | W19-2710 | 7 | [] | 1.585181 | |
12 documents annotated signal instances | W19-2710 | 4 | ## 2.2 The Signal Anchoring Mechanism
As mentioned in Section 1, RST-SC does not provide information about the location of discourse signals. Thus, Liu and Zeldes (2019) presented an annotation effort to anchor signal tokens in the text, with six categories being annotated. Their results showed that with 11 documents ... | 0 | false | {
"start": [],
"end": [],
"text": []
} | retrieved | 4 | W19-2710 | 7 | [] | 1.585181 | |
12 documents annotated signal instances | D19-1680 | 6 | ## 4.3 Annotating intervention instances
We provided definitions and text examples for the intervention types 7 to two annotators, and then asked them to identify and annotate intervention instances for each document. Annotators are provided with a User Interface (UI) (Chan et al., 2019) which allows them to search fo... | 0 | false | {
"start": [],
"end": [],
"text": []
} | retrieved | 5 | W19-2710 | 7 | [] | 1.585181 | |
random indexing | W11-4631 | 3 |
One important advantage of the particular random indexing approach is that the full n × d feature matrix F never needs to be explicitly computed or represented (Karlgren and Sahlgren, 2001). As described above, with RI we construct the representation of the data in G by incrementally accumulating the index vectors as... | 1 | true | {
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2
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"text": [
"One important advantage of the particular random indexing approach is that the full n × d feature matrix F never needs to be explicitly computed or represented (Karlgren and Sahlgren, 2001). As described above, with RI we construct the representation of... | retrieved | 1 | S16-2024 | 13 | [
"One important advantage of the particular random indexing approach is that the full n × d feature matrix F never needs to be explicitly computed or represented (Karlgren and Sahlgren, 2001). As described above, with RI we construct the representation of the data in G by incrementally accumulating the index vectors... | 2.674618 | |
random indexing | W11-4631 | 10 | ## References
- Dimitris Achlioptas. 2001. Database-friendly random projections. In Proceedings of the Twentieth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems , Santa Barbara, USA.
- Bernd Bohnet. 2010. Top accuracy and fast dependency parsing is not a contradiction. In Proceedings of the 23rd I... | 0 | false | {
"start": [],
"end": [],
"text": []
} | retrieved | 3 | S16-2024 | 13 | [] | 2.674618 | |
Hindi corpus 65 phrases number of classes | C69-6214 | 0 | Abstract
AN APPLICATION OP COMPUTER TECHNIQUES TO ANALYSIS OF THE VERB PHRASE IN HINDI AND ENGLISH: A Preliminary Report
Dr, LoM, Khubohandanl and WoW. Glover
Authors worked on the Project at ~oona, India with the facilities of the computer CDC 3600-160A installed at the Tats Institute for Fundamental Research, Bomb... | 1 | true | {
"start": [
884
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"text": [
"The results obtained with a criterion for classification of \"identical context one-deep on both sides\" were quite satisfactory. In Hindi 25 classes were formed from the corpus of 65 phrases."
]
} | gold | -1 | C69-6214 | -1 | [
"The results obtained with a criterion for classification of \"identical context one-deep on both sides\" were quite satisfactory. In Hindi 25 classes were formed from the corpus of 65 phrases."
] | 0.368831 | |
Hindi corpus 65 phrases number of classes | I11-1013 | 5 | ## 3.2 Verb Classing
Using a segmenter, the root verb is separated from its inflected suffix for all the extracted verb phrases. These extracted verb phrases are then clustered based on the root verb so that all the variations of a root verb ' < verb > ' are grouped together into one cluster. As an example, a part of ... | 0 | false | {
"start": [],
"end": [],
"text": []
} | retrieved | 2 | C69-6214 | -1 | [] | 0.368831 | |
Hindi corpus 65 phrases number of classes | I08-5004 | 12 | ## 8.2 Results on the Test Data
The best identified feature set is used for the development of the NER systems for all the five languages. We have already mentioned that for only for Bengali and Hindi we have added linguistic rules
and gazetteer lists in the MaxEnt based NER systems. The accuracy of the system on the... | 0 | false | {
"start": [],
"end": [],
"text": []
} | retrieved | 3 | C69-6214 | -1 | [] | 0.368831 | |
Hindi corpus 65 phrases number of classes | W17-0118 | 12 |
Table 5: Class sizes. When there is no example in the curated set (C), an example is taken from the hypothesized set output by the system.
| Class | Example | C | GL (in C) | CB (in C) |
|---------|--------------------|-----|-------------|-------------|
| 0001 | tatuk 'have fever' | 0 | 29 (1) | 11 (0) |
| 0010 | ta... | 0 | false | {
"start": [],
"end": [],
"text": []
} | retrieved | 4 | C69-6214 | -1 | [] | 0.368831 | |
Hindi corpus 65 phrases number of classes | I11-1013 | 7 | ## 3.4 Generation of Verb Phrase Dictionary
Given the root verb mapping and the classes to which these source and target root verb belong to, we create a 'source class' to 'target class' mapping, or a 'verb-pair class', by replacing the root verbs with their corresponding verb classes. This causes each of the verb pai... | 0 | false | {
"start": [],
"end": [],
"text": []
} | retrieved | 5 | C69-6214 | -1 | [] | 0.368831 | |
research approach discussed in the paper | W94-0307 | 0 | ## Content and Rhetorical Status Selection in Instructional Texts
Leila Kosseim kosseim@iro.umontreal.ca
Guy Lapalme lapalme@iro.umontreal.ca
D4partement d'informatique et de recherche
op4rationnelle
Universitd e Montrdal
PB 6128, Succ. Centre Ville
Montrdal, Qudbec, Canada H3C 3J7
be represented as in figure 1... | 1 | true | {
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"This paper discusses an approach to planning the content of instructional texts. The research is based on a corpus study of 15 French procedural texts ranging from step--bystep device manuals to general artistic procedures. The approach taken starts f... | gold | -1 | W94-0307 | -1 | [
"This paper discusses an approach to planning the content of instructional texts. The research is based on a corpus study of 15 French procedural texts ranging from step--bystep device manuals to general artistic procedures. The approach taken starts from an AI task planner building a task representation, from whic... | 3.18098 | |
research approach discussed in the paper | O14-5000 | 52 | ## B.1 背景文步
follow NE ( CD ) , NE ( CD ) show that NE ( CD ) demonstrate that NE ( CD ) propose model it be , however , there be , however , to knowledge , there be to good of knowledge , in case , however , NE ( CD ) present NE ( CD ) describe however , in case , to knowledge , this be collection comprise CD in pract... | 0 | false | {
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} | retrieved | 1 | W94-0307 | -1 | [] | 3.18098 | |
research approach discussed in the paper | 2023.findings-emnlp.182 | 19 | ## Limitations
In this paper, we have discussed two prominent sources of hallucination for LLMs in natural language inference tasks. We acknowledge that this is not an exhaustive search of all the sources, where further exploration should be done in future work.
We also note that after controlling for the factors dis... | 1 | true | {
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"text": [
"As discussed in Appendix A, we compared a range of popular LLM prompting techniques and selected the most promising approach."
]
} | retrieved | 2 | W94-0307 | -1 | [
"As discussed in Appendix A, we compared a range of popular LLM prompting techniques and selected the most promising approach."
] | 3.18098 | |
research approach discussed in the paper | 2023.nlp4dh-1.12 | 6 | ## 2.3 Research process
Figure 1 presents the key phases in the research process. During the study, I conducted both distant reading and traditional close reading in parallel (Jänicke et al., 2015). During the distant reading phase, I utilised computational methods to analyse the material based on topics and keywords,... | 1 | true | {
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1462
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"Figure 1 presents the key phases in the research process. During the study, I conducted both distant reading and traditional close reading in parallel (Jänicke et al., 2015). During the distant reading phase, I utilised computational... | retrieved | 3 | W94-0307 | -1 | [
"Figure 1 presents the key phases in the research process. During the study, I conducted both distant reading and traditional close reading in parallel (Jänicke et al., 2015). During the distant reading phase, I utilised computational methods to analyse the material based on topics and keywords, enabling a systemat... | 3.18098 | |
Transformer aspect extraction hotel reviews example | 2024.stil-1.31 | 2 | ## 2. Trabalhos Relacionados
Os trabalhos de identificac ¸ ˜ ao de aspectos para o portuguˆ es se baseiam, principalmente, no uso de l´ exicos [Costa and Pardo 2020], regras de linguagem [Vargas and Pardo 2020, Machado et al. 2021], em algoritmos de aprendizado de m´ aquina tradicionais [Balage Filho 2017, Vargas and ... | 1 | true | {
"start": [
445
],
"end": [
608
],
"text": [
"Em [Resplande et al. 2022], por exemplo, os autores avaliaram o uso de modelos baseados em Transformers na extrac ¸˜ ao de aspectos em avaliac ¸ ˜ oes de hot´ eis."
]
} | gold | -1 | 2024.stil-1.31 | 2 | [
"Em [Resplande et al. 2022], por exemplo, os autores avaliaram o uso de modelos baseados em Transformers na extrac ¸˜ ao de aspectos em avaliac ¸ ˜ oes de hot´ eis."
] | 1.681122 | |
Transformer aspect extraction hotel reviews example | 2023.konvens-main.21 | 0 | ## Aspect-Based Sentiment Analysis as a Multi-Label Classification Task on the Domain of German Hotel Reviews
## Jakob Fehle
Media Informatics Group University of Regensburg Regensburg, Germany jakob.fehle@ur.de
## Thomas Schmidt
Media Informatics Group University of Regensburg Regensburg, Germany thomas.schmidt@ur... | 1 | true | {
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"text": [
"This paper addresses this gap by utilizing BERT-based transformer models, known for their exceptional performance in context-sensitive natural language processing tasks, to perform ABSA in a multi-label classification setting. We demonstrate our appr... | retrieved | 1 | 2024.stil-1.31 | 2 | [
"This paper addresses this gap by utilizing BERT-based transformer models, known for their exceptional performance in context-sensitive natural language processing tasks, to perform ABSA in a multi-label classification setting. We demonstrate our approach on a novel dataset of German hotel reviews that we have coll... | 1.681122 | |
Transformer aspect extraction hotel reviews example | 2025.findings-acl.1273 | 25 | ## Aspect Identification: Rooms
You are good at understanding documents with hotel review opinions.
Below is a business review for a hotel, please extract fragments that are related to Rooms of the hotel.
Definition of Rooms:
Assessment of how well the room meets the guest's needs and expectations in terms of comfo... | 1 | true | {
"start": [
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"end": [
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"text": [
"Figure 17: The prompt of Aspect Identification for the review aspect of Rooms ."
]
} | retrieved | 2 | 2024.stil-1.31 | 2 | [
"Figure 17: The prompt of Aspect Identification for the review aspect of Rooms ."
] | 1.681122 | |
Transformer aspect extraction hotel reviews example | 2023.konvens-main.21 | 3 | ## 2 Related Work
Over the last decade, ABSA has experienced significant growth through different shared task workshops, such as the SemEval Shared Tasks for the English language from 2014 to 2016 (Pontiki et al., 2014, 2015, 2016), stimulating the development of various methods addressing the three fundamental subtas... | 1 | true | {
"start": [
1325
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"end": [
1681
],
"text": [
"In particular, since the first SemEval workshop on ABSA in 2014, the number of accessible datasets for the English language has significantly increased, covering various domains with different levels of annotation complexity, such as hotel reviews (... | retrieved | 3 | 2024.stil-1.31 | 2 | [
"In particular, since the first SemEval workshop on ABSA in 2014, the number of accessible datasets for the English language has significantly increased, covering various domains with different levels of annotation complexity, such as hotel reviews (Yin et al., 2017), financial microblogs (Maia et al., 2018), and A... | 1.681122 | |
What is HotpotQA | 2023.acl-long.89 | 22 |
Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu, Paul N. Bennett, Junaid Ahmed, and Arnold Overwijk. 2021a. Approximate nearest neighbor negative contrastive learning for dense text retrieval. In International Conference on Learning Representations .
Wenhan Xiong, Xiang Lorraine Li, Srinivasan Iyer, Jing... | 1 | true | {
"start": [
575
],
"end": [
944
],
"text": [
"Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. 2018. HotpotQA: A dataset for diverse, explainable multi-hop question answering. In Proceedings of the 2018 Conference on Empirical M... | gold | -1 | 2023.acl-long.89 | 22 | [
"Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. 2018. HotpotQA: A dataset for diverse, explainable multi-hop question answering. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing , pages 2369-2380, Brussel... | 1.081433 | |
What is HotpotQA | D18-1259 | 2 | ## 2 Data Collection
The main goal of our work is to collect a diverse and explainable question answering dataset that requires multi-hop reasoning. One way to do so is to define reasoning chains based on a knowledge base (Welbl et al., 2018; Talmor and Berant, 2018). However, the resulting datasets are limited by the... | 1 | true | {
"start": [
22
],
"end": [
149
],
"text": [
"The main goal of our work is to collect a diverse and explainable question answering dataset that requires multi-hop reasoning."
]
} | retrieved | 1 | 2023.acl-long.89 | 22 | [
"The main goal of our work is to collect a diverse and explainable question answering dataset that requires multi-hop reasoning."
] | 1.081433 | |
What is HotpotQA | 2022.coling-1.518 | 7 | ## 4 Data Analysis
## 4.1 Dataset Statistics
The final dataset consists of 1,034 high-quality data samples, in which 515 samples come from the field of Biology, 401 from the field of chemistry, 88 from the field of physics, 19 from the field of electricalengineering, 7 from the field of environmentalscience, and 4 fr... | 1 | true | {
"start": [
508
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"end": [
796
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"text": [
"To investigate the depth and diversity of questions in KHANQ, we classify questions based on the first two words in the question and compare them to other commonly-used question generation datasets: SQuAD 2.0 (Rajpurkar et al., 2018) and HotpotQA (Yan... | retrieved | 2 | 2023.acl-long.89 | 22 | [
"To investigate the depth and diversity of questions in KHANQ, we classify questions based on the first two words in the question and compare them to other commonly-used question generation datasets: SQuAD 2.0 (Rajpurkar et al., 2018) and HotpotQA (Yang et al., 2018), as shown in Table 2."
] | 1.081433 | |
What is HotpotQA | D18-1259 | 15 | ## A.3 Crowd Worker Interface
Our crowd worker interface is based on ParlAI (Miller et al., 2017), an open-source project that facilitates the development of dialog systems and data collection with a dialog interface. We adapt ParlAI for collecting question answer pairs by converting the collection workflow into a sys... | 1 | true | {
"start": [
795
],
"end": [
854
],
"text": [
"Figure 5: Distribution of lengths of questions in HOTPOTQA."
]
} | retrieved | 3 | 2023.acl-long.89 | 22 | [
"Figure 5: Distribution of lengths of questions in HOTPOTQA."
] | 1.081433 | |
What is HotpotQA | D18-1259 | 16 | ## B Further Data Analysis
To further look into the diversity of the data in HOTPOTQA, we further visualized the distribution of question lengths in the dataset in Figure 5. Besides being diverse in terms of types as is show in the main text, questions also vary greatly in length, indicating different levels of comple... | 0 | false | {
"start": [],
"end": [],
"text": []
} | retrieved | 5 | 2023.acl-long.89 | 22 | [] | 1.081433 | |
What is HotpotQA | 2023.acl-long.89 | 22 |
Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu, Paul N. Bennett, Junaid Ahmed, and Arnold Overwijk. 2021a. Approximate nearest neighbor negative contrastive learning for dense text retrieval. In International Conference on Learning Representations .
Wenhan Xiong, Xiang Lorraine Li, Srinivasan Iyer, Jing... | 1 | true | {
"start": [
575
],
"end": [
944
],
"text": [
"Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. 2018. HotpotQA: A dataset for diverse, explainable multi-hop question answering. In Proceedings of the 2018 Conference on Empirical M... | gold | -1 | 2023.acl-long.89 | 22 | [
"Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. 2018. HotpotQA: A dataset for diverse, explainable multi-hop question answering. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing , pages 2369-2380, Brussel... | 0.993376 | |
What is HotpotQA | D18-1259 | 2 | ## 2 Data Collection
The main goal of our work is to collect a diverse and explainable question answering dataset that requires multi-hop reasoning. One way to do so is to define reasoning chains based on a knowledge base (Welbl et al., 2018; Talmor and Berant, 2018). However, the resulting datasets are limited by the... | 1 | true | {
"start": [
22
],
"end": [
149
],
"text": [
"The main goal of our work is to collect a diverse and explainable question answering dataset that requires multi-hop reasoning."
]
} | retrieved | 1 | 2023.acl-long.89 | 22 | [
"The main goal of our work is to collect a diverse and explainable question answering dataset that requires multi-hop reasoning."
] | 0.993376 | |
What is HotpotQA | 2022.coling-1.518 | 7 | ## 4 Data Analysis
## 4.1 Dataset Statistics
The final dataset consists of 1,034 high-quality data samples, in which 515 samples come from the field of Biology, 401 from the field of chemistry, 88 from the field of physics, 19 from the field of electricalengineering, 7 from the field of environmentalscience, and 4 fr... | 1 | true | {
"start": [
508
],
"end": [
796
],
"text": [
"To investigate the depth and diversity of questions in KHANQ, we classify questions based on the first two words in the question and compare them to other commonly-used question generation datasets: SQuAD 2.0 (Rajpurkar et al., 2018) and HotpotQA (Yan... | retrieved | 2 | 2023.acl-long.89 | 22 | [
"To investigate the depth and diversity of questions in KHANQ, we classify questions based on the first two words in the question and compare them to other commonly-used question generation datasets: SQuAD 2.0 (Rajpurkar et al., 2018) and HotpotQA (Yang et al., 2018), as shown in Table 2."
] | 0.993376 | |
What is HotpotQA | D18-1259 | 15 | ## A.3 Crowd Worker Interface
Our crowd worker interface is based on ParlAI (Miller et al., 2017), an open-source project that facilitates the development of dialog systems and data collection with a dialog interface. We adapt ParlAI for collecting question answer pairs by converting the collection workflow into a sys... | 1 | true | {
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"end": [
854
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"text": [
"Figure 5: Distribution of lengths of questions in HOTPOTQA."
]
} | retrieved | 3 | 2023.acl-long.89 | 22 | [
"Figure 5: Distribution of lengths of questions in HOTPOTQA."
] | 0.993376 | |
What is HotpotQA | D18-1259 | 16 | ## B Further Data Analysis
To further look into the diversity of the data in HOTPOTQA, we further visualized the distribution of question lengths in the dataset in Figure 5. Besides being diverse in terms of types as is show in the main text, questions also vary greatly in length, indicating different levels of comple... | 0 | false | {
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purpose of this document | 2023.wassa-1.17 | 11 | ## Acknowledgements
This paper was prepared for informational purposes in part by the Artificial Intelligence Research Group of JPMorgan Chase & Co and its affiliates ('J.P. Morgan') and is not a product of the Research Department of J.P. Morgan. J.P. Morgan makes no representation and warranty whatsoever and disclaim... | 1 | true | {
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"This paper was prepared for informational purposes in part by the Artificial Intelligence Research Group of JPMorgan Chase & Co and its affiliates ('J.P. Morgan') and is not a product of the Research Department of J.P. Morgan. J.P. Morgan makes no repr... | gold | -1 | 2023.wassa-1.17 | 11 | [
"This paper was prepared for informational purposes in part by the Artificial Intelligence Research Group of JPMorgan Chase & Co and its affiliates ('J.P. Morgan') and is not a product of the Research Department of J.P. Morgan. J.P. Morgan makes no representation and warranty whatsoever and disclaims all liability,... | 1.425849 | |
purpose of this document | J95-1002 | 6 | ## PURPOSE (taken from Mann and Thompson 1987)
constraints on N:
presents an activity
constraints on S:
presents a situation that is unrealized
constraints
on the N+S combination:
S presents a situation to be realized through the activity in N R recognizes that the activity in N is initiated in order to realize ... | 0 | false | {
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"end": [],
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} | retrieved | 1 | 2023.wassa-1.17 | 11 | [] | 1.425849 | |
purpose of this document | 2025.acl-long.1386 | 25 | ## B Detailed annotation guideline provided to the annotators
## B.1 Introduction
The purpose of this work is to extract Adverse Events (AE) in elderly patients' electronic health records (discharge summaries) to be used as a novel source of data for health and social care research. In order to automate this process,... | 1 | true | {
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"The purpose of this work is to extract Adverse Events (AE) in elderly patients' electronic health records (discharge summaries) to be used as a novel source of data for health and social care research. In order to automate this process, we rely on Natu... | retrieved | 2 | 2023.wassa-1.17 | 11 | [
"The purpose of this work is to extract Adverse Events (AE) in elderly patients' electronic health records (discharge summaries) to be used as a novel source of data for health and social care research. In order to automate this process, we rely on Natural Language Processing (NLP), a sub-field of artificial intell... | 1.425849 | |
purpose of this document | 2009.mtsummit-caasl.10 | 11 | ##
## ... | 0 | false | {
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} | retrieved | 3 | 2023.wassa-1.17 | 11 | [] | 1.425849 | |
purpose of this document | P79-1014 | 7 | ## Im,*|i@m|li,I@Wm~#mI~@Igm#wIiII#mmimmIII|@milIIillJgimR@
IPP does not consider the $ S H O O T script to be a total explanation of a snootin~ event. It requires a representation wnlcn indicates the purpose of the various actors, in the absence of any other information, IPP assu~es people wno shoot are deliberately ... | 0 | false | {
"start": [],
"end": [],
"text": []
} | retrieved | 5 | 2023.wassa-1.17 | 11 | [] | 1.425849 | |
SimSum research project ultimate goal | W97-0714 | 11 | ## 5 Conclusion
Advancing the sc~entfftc frontiers of text summanzaUon presupposes more knowledge about the way summartzatton works The mare frmt of the empmcal mvesUgat~on be- hind SlmSum is an tmage of the summanzalaon process which Is detmled enough to lay the foundattons for a stmulat~on Since the resulting summar... | 1 | true | {
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"end": [
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"text": [
"The mare frmt of the empmcal mvesUgat~on be- hind SlmSum is an tmage of the summanzalaon process which Is detmled enough to lay the foundattons for a stmulat~on Since the resulting summarization model incorporates the know-how of human experts, tt has... | gold | -1 | W97-0714 | 11 | [
"The mare frmt of the empmcal mvesUgat~on be- hind SlmSum is an tmage of the summanzalaon process which Is detmled enough to lay the foundattons for a stmulat~on Since the resulting summarization model incorporates the know-how of human experts, tt has good prospects of presenting powerful techmques Summarizing by ... | 1.093513 | |
SimSum research project ultimate goal | 2023.acl-long.552 | 21 | ## 9 Conclusions and Future Work
In this paper, we propose SIMSUM, a new model for document-level text simplification. We demonstrate that SIMSUM sets a new state of the art on document simplification outperforming the previously competitive MUSS baseline in terms of SARI and D-SARI scores. We also release cleaned ver... | 1 | true | {
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"text": [
"In this paper, we propose SIMSUM, a new model for document-level text simplification. We demonstrate that SIMSUM sets a new state of the art on document simplification outperforming the previously competitive MUSS baseline in terms of SARI and D-SARI s... | retrieved | 1 | W97-0714 | 11 | [
"In this paper, we propose SIMSUM, a new model for document-level text simplification. We demonstrate that SIMSUM sets a new state of the art on document simplification outperforming the previously competitive MUSS baseline in terms of SARI and D-SARI scores. We also release cleaned versions of two existing large-s... | 1.093513 | |
SimSum research project ultimate goal | 2023.acl-long.552 | 19 | ## 7 Human Evaluation
In addition to the automatic evaluation, we performed a human evaluation of the outputs from different models. We run the assessment on 50 randomly selected samples from each dataset, thus 100 in total. We recruited two expert human evaluators to independently evaluate the generated texts from se... | 0 | false | {
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} | retrieved | 2 | W97-0714 | 11 | [] | 1.093513 | |
SimSum research project ultimate goal | 2023.acl-long.552 | 39 | ## C /square ✓ Did you run computational experiments?
5, 6
- /square ✓ C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Appendix
The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addi... | 0 | false | {
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"end": [],
"text": []
} | retrieved | 3 | W97-0714 | 11 | [] | 1.093513 | |
ELMo EEBO configuration F1 score NPR tag | 2022.findings-naacl.44 | 16 | ## 10 Function tag analysis
Table 6 shows the score for the function tags over the different configurations, with either 10 or 31 function tags. The overall score for the function tags drops drastically between ftags-10 and ftags31. For example, on the test sections, with ELMo EEBO it falls from a mean of 97.90% with ... | 0 | false | {
"start": [],
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} | retrieved | 1 | 2022.findings-naacl.44 | 12 | [] | 4.734863 | |
ELMo EEBO configuration F1 score NPR tag | 2025.africanlp-1.31 | 8 | ## 4.3 Evaluation
We use F 1 score to evaluate our models. We only evaluate morphological tagging performance, as opposed to full morphological parsing (segmentation + tagging). However, tagging inherently depends on segmentation in our setup, since models are trained on the pre-segmented morpheme sequences.
In our m... | 0 | false | {
"start": [],
"end": [],
"text": []
} | retrieved | 2 | 2022.findings-naacl.44 | 12 | [] | 4.734863 | |
ELMo EEBO configuration F1 score NPR tag | 2025.findings-naacl.258 | 34 | ## G ELO Ratings Computation
For phase I, the total number of evaluations are 36 by each annotator and we consider each annotation as a single match. In Phase II, 63 additional annotations are conducted making a total of 135 matches for computing the ELO ratings. For every match the new rating :
<!-- formula-not-deco... | 0 | false | {
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"end": [],
"text": []
} | retrieved | 4 | 2022.findings-naacl.44 | 12 | [] | 4.734863 | |
ELMo EEBO configuration F1 score NPR tag | 2022.findings-naacl.44 | 11 | ## 7 Pretraining comparison experiments
Table 2 presents parsing results for the dev/test sections of the 8 cross-validation splits described in Section 4. The rows are the four embedding representations described in Section 6.1 and the columns are the F1 scores (evalb bracket scores, as discussed in Section 6.3) for ... | 0 | false | {
"start": [],
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"text": []
} | retrieved | 5 | 2022.findings-naacl.44 | 12 | [] | 4.734863 | |
Translation Quality Score definition | 2025.emnlp-main.1018 | 3 | ## 3 Human re-evaluation
To investigate the translation quality of the FLORES+ benchmark, we manually re-evaluated translations of the four genealogically, orthographically, and geographically diverse languages with varying resource availability: Asante Twi, Japanese, Jinghpaw, and South Azerbaijani. See Table 1 for d... | 1 | true | {
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"The original work (Goyal et al., 2022) uses a metric called Translation Quality Score (TQS) to evaluate the translation quality; however, the detailed definition of this metric is not explained in their work. For this reason, we tentatively define T... | gold | -1 | 2025.emnlp-main.1018 | 3 | [
"The original work (Goyal et al., 2022) uses a metric called Translation Quality Score (TQS) to evaluate the translation quality; however, the detailed definition of this metric is not explained in their work. For this reason, we tentatively define TQS as follows:\n\n<!-- formula-not-decoded -->\n\nwhere C is the n... | 2.158995 | |
Translation Quality Score definition | 2013.tc-1.6 | 2 | ## 2. Defining quality
The definition of translation quality has long been an issue in academic translation studies. Much of the traditional focus in (human) translation studies has been derived from literary translation practice assuming the existence of absolute quality and the availability of unlimited resources. I... | 0 | false | {
"start": [],
"end": [],
"text": []
} | retrieved | 1 | 2025.emnlp-main.1018 | 3 | [] | 2.158995 | |
Translation Quality Score definition | 2013.tc-1.6 | 8 | ## 4. Scoring
Because the ability to generate scores is important in many environments, MQM provides a scoring system, defined as described below. The following basic formula is used for calculating MQM quality scores in an error-count environment:
<!-- formula-not-decoded -->
Where:
- TQ = quality score. The overa... | 1 | true | {
"start": [
15
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"end": [
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"text": [
"Because the ability to generate scores is important in many environments, MQM provides a scoring system, defined as described below. The following basic formula is used for calculating MQM quality scores in an error-count environment:\n\n<!-- formula-... | retrieved | 2 | 2025.emnlp-main.1018 | 3 | [
"Because the ability to generate scores is important in many environments, MQM provides a scoring system, defined as described below. The following basic formula is used for calculating MQM quality scores in an error-count environment:\n\n<!-- formula-not-decoded -->\n\nWhere:\n\n- TQ = quality score. The overall r... | 2.158995 | |
Translation Quality Score definition | 2020.eamt-1.23 | 2 | ## 2 Background
How to tell whether a translation is good or bad is one of the most important and one of the most difficult questions asked in connection with translation. Best practices for evaluating HT and MT differ, and assessments of human-machine parity have largely ignored the former.
## 2.1 Evaluation of HT
... | 1 | true | {
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"text": [
"The quality score of a given translation is computed as a linear combination of error counts and severity levels (i.e., weights). The error categories are defined in the quality standard; the number of errors per category and the severity of each er... | retrieved | 3 | 2025.emnlp-main.1018 | 3 | [
"The quality score of a given translation is computed as a linear combination of error counts and severity levels (i.e., weights). The error categories are defined in the quality standard; the number of errors per category and the severity of each error are determined by a single qualified rater. A translation is c... | 2.158995 | |
Translation Quality Score definition | W18-2005 | 2 | ## What do we mean by translation quality?
## Well, what do we mean by 'translation'?
- A translation (product) is target-language content corresponds to source-language content
- Must include text
- May include non-textual elements such as audio-visual content and software components
- Translation (process) is the a... | 0 | false | {
"start": [],
"end": [],
"text": []
} | retrieved | 4 | 2025.emnlp-main.1018 | 3 | [] | 2.158995 | |
open access scientific publications definition | J08-4008 | 1 | ## 1. Why Open Access?
There are a number of definitions of the term 'open access' in circulation, but almost all share the key principle that scientific literature should be freely available for all to read, download, copy, distribute, and use (with appropriate attribution) without restriction. At the time of writing... | 1 | true | {
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"text": [
"There are a number of definitions of the term 'open access' in circulation, but almost all share the key principle that scientific literature should be freely available for all to read, download, copy, distribute, and use (with appropriate attribution)... | retrieved | 1 | P00-1021 | 17 | [
"There are a number of definitions of the term 'open access' in circulation, but almost all share the key principle that scientific literature should be freely available for all to read, download, copy, distribute, and use (with appropriate attribution) without restriction."
] | 0.445962 | |
open access scientific publications definition | 2024.sdp-1.15 | 5 | ## 2.3 Scientific Article Metadata
Recent datasets have made available open access publications, including their full text and figures, such as the PubMed Open Access Subset (National Library of Medicine, 2003). Datasets of scientific publications can give us access to metadata in an easy-to-use format. For example, O... | 0 | false | {
"start": [],
"end": [],
"text": []
} | retrieved | 2 | P00-1021 | 17 | [] | 0.445962 | |
open access scientific publications definition | 2023.nlp4dh-1.16 | 5 | ## 3.1 Open Science
Most academics in any discipline would agree that Open science is a good thing allowing everyone access to research results and makes these results more transparent. However, for most humanities scholars open science in practice tends to be limited to paying open-access journal publication fees. Co... | 0 | false | {
"start": [],
"end": [],
"text": []
} | retrieved | 3 | P00-1021 | 17 | [] | 0.445962 | |
open access scientific publications definition | 2024.sdp-1.15 | 2 |
To harness the richness found in images, we propose creating a new vision text dual encoder model to improve the performance of image retrieval tasks in scientific publications. We develop a dataset of scientific image captions based on open-access articles from PubMed Open Access Subset (National Library of Medicine... | 0 | false | {
"start": [],
"end": [],
"text": []
} | retrieved | 4 | P00-1021 | 17 | [] | 0.445962 | |
open access scientific publications definition | Y08-1005 | 6 | ## 4. Diversion: Open Source
While scientific achievement throughout history has often provided the potential for direct financial reward, that potential is great today, and is particular significant in computational linguistics. That profit potential unfortunately leads many researchers and their institutions to cont... | 0 | false | {
"start": [],
"end": [],
"text": []
} | retrieved | 5 | P00-1021 | 17 | [] | 0.445962 | |
example of model error in qualitative analysis | 2025.banglalp-1.8 | 6 | ## 5.1 Error Analysis
A comprehensive quantitative and qualitative error analysis is conducted to provide detailed insights into the proposed model's performance.
## 5.1.1 Quantitative Analysis
The last row of Table 5 shows a misclassification example. Here, the model mistakenly labels a depressive text as non\_depr... | 1 | true | {
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"The last row of Table 5 shows a misclassification example. Here, the model mistakenly labels a depressive text as non\\_depressive. The sentence expresses strong criticism and frustration toward systemic issues, su... | retrieved | 2 | P18-1135 | 11 | [
"The last row of Table 5 shows a misclassification example. Here, the model mistakenly labels a depressive text as non\\_depressive. The sentence expresses strong criticism and frustration toward systemic issues, such as business syndicates and government accountability",
"oposed model. In the first and second te... | 3.551814 | |
example of model error in qualitative analysis | 2024.naacl-long.115 | 12 | ## 5.2 Qualitative Samples
QUALEVAL also allows model developers to extract prominent qualitative examples that can aid in the modeling lifecycle. Given that both in an academic and industry setting, understanding representative instances of ground truth and model-generated answers is important, QUALEVAL automates tha... | 1 | true | {
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"Figure 7 shows qualitative samples from the MBPP dataset generated by the DAVINCI-3 (left and center) and DAVINCI-2 (right) models. In the first example, the ground truth program uses XOR to test for uniqueness, while the generation uses a loop to ch... | retrieved | 3 | P18-1135 | 11 | [
"Figure 7 shows qualitative samples from the MBPP dataset generated by the DAVINCI-3 (left and center) and DAVINCI-2 (right) models. In the first example, the ground truth program uses XOR to test for uniqueness, while the generation uses a loop to check for uniqueness. In the second example, the ground truth progr... | 3.551814 | |
Unigram assumption about words | C12-1021 | 10 | ## 3.2 Assumptions about relations between word tokens
The simplest assumption about the relation between words within an utterance is probably that there is none. For a probabilistic model, this leads to a Unigram assumption about words, i.e. that the probability of a sequence of words is simply the product of the pr... | 1 | true | {
"start": [
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"text": [
"The simplest assumption about the relation between words within an utterance is probably that there is none. For a probabilistic model, this leads to a Unigram assumption about words, i.e. that the probability of a sequence of words is simply the produ... | gold | -1 | C12-1021 | 10 | [
"The simplest assumption about the relation between words within an utterance is probably that there is none. For a probabilistic model, this leads to a Unigram assumption about words, i.e. that the probability of a sequence of words is simply the product of the probability of each individual word, irrespective of ... | 1.677144 | |
Unigram assumption about words | 2021.findings-acl.326 | 2 | ## 2 The Unigram Distribution
The unigram distribution is a probability distribution over the possible word forms in a language's lexicon. This probability takes the frequency of a token into account, assigning larger probabilities to word forms which are more likely to be
3 As a final contribution of our work, the c... | 0 | false | {
"start": [],
"end": [],
"text": []
} | retrieved | 1 | C12-1021 | 10 | [] | 1.677144 | |
Unigram assumption about words | 2021.findings-acl.326 | 3 | ## 2.1 Complex Vocabularies
The composition of spoken vocabularies is structured according to a host of factors. Stemming from articulatory biases, each language has a set of constraints on what sequences of speech sounds can be valid words in it; this is termed the phonotactics of a language. Languages also exhibit s... | 0 | false | {
"start": [],
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"text": []
} | retrieved | 3 | C12-1021 | 10 | [] | 1.677144 | |
why use competency questions in ontology authoring | W15-0402 | 5 |
From our survey of the literature, we conclude that there are few good logic-driven approaches to entailment selection. Therefore, for our interface we plan to investigate the syntactic selection method of Denaux et al. (2012), together with a preference ordering based on linear discourse structure and the user's goa... | 1 | true | {
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"To tackle this problem, we are incorporating the technique of Competency Question-driven Ontology Authoring (CQOA) (Ren et al., 2014) into our dialogue system. This new technique takes 'Competency Questions' as requirements for o... | gold | -1 | W15-0402 | 5 | [
"To tackle this problem, we are incorporating the technique of Competency Question-driven Ontology Authoring (CQOA) (Ren et al., 2014) into our dialogue system. This new technique takes 'Competency Questions' as requirements for ontologies and uses them to automatically generate authoring tests for ensuring the qua... | 0.913233 | |
why use competency questions in ontology authoring | 2021.cnl-1.11 | 14 | ## Acknowledgments
This work was financially supported by Hasso Plattner Institute for Digital Engineering through the HPI Research School at UCT. Many thanks to Prof BE Antia for his help with the linguistic analysis.
## References
Camila Zacch´ e de Aguiar, Ricardo de Almeida Falbo, and V´ ıtor E Silva Souza. 2019... | 0 | false | {
"start": [],
"end": [],
"text": []
} | retrieved | 1 | W15-0402 | 5 | [] | 0.913233 | |
why use competency questions in ontology authoring | 2025.ldk-1.15 | 16 | ## References
Reham Alharbi, Valentina Tamma, Floriana Grasso, and Terry R. Payne. 2023. An experiment in retrofitting competency questions for existing ontologies. ArXiv , abs/2311.05662.
Reham Alharbi, Valentina Tamma, Floriana Grasso, and Terry R. Payne. 2024a. An experiment in retrofitting competency questions fo... | 0 | false | {
"start": [],
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"text": []
} | retrieved | 2 | W15-0402 | 5 | [] | 0.913233 | |
2018 Robotic Scene Segmentation Challenge | 2025.naacl-long.500 | 18 |
Gupta, Vignesh Ramanathan, Viktor Kerkez, Vincent Gonguet, Virginie Do, Vish Vogeti, Vladan Petrovic, Weiwei Chu, Wenhan Xiong, Wenyin Fu, Whitney Meers, Xavier Martinet, Xiaodong Wang, Xiaoqing Ellen Tan, Xinfeng Xie, Xuchao Jia, Xuewei Wang, Yaelle Goldschlag, Yashesh Gaur, Yasmine Babaei, Yi Wen, Yiwen Song, Yuche... | 0 | false | {
"start": [],
"end": [],
"text": []
} | retrieved | 1 | 2024.lrec-main.63 | 20 | [] | 0.52398 | |
2018 Robotic Scene Segmentation Challenge | 2025.naacl-long.500 | 17 | ## References
Florian Barth and Tillmann Dönicke. 2021. Participation in the konvens 2021 shared task on scene segmentation using temporal, spatial and entity feature vectors. In Shared Task on Scene Segmentation .
Parishad BehnamGhader, Vaibhav Adlakha, Marius Mosbach, Dzmitry Bahdanau, Nicolas Chapados, and Siva Re... | 0 | false | {
"start": [],
"end": [],
"text": []
} | retrieved | 3 | 2024.lrec-main.63 | 20 | [] | 0.52398 | |
2018 Robotic Scene Segmentation Challenge | 2025.acl-long.695 | 1 | ## Abstract
Traditional reinforcement learning-based robotic control methods are often taskspecific and fail to generalize across diverse environments or unseen objects and instructions. Visual Language Models (VLMs) demonstrate strong scene understanding and planning capabilities but lack the ability to generate acti... | 0 | false | {
"start": [],
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"text": []
} | retrieved | 4 | 2024.lrec-main.63 | 20 | [] | 0.52398 | |
2018 Robotic Scene Segmentation Challenge | 2025.naacl-long.500 | 2 | ## 2 Task Description
The task of automatic scene segmentation was formally defined by Zehe et al. (2021a). The goal is to segment a literary text into scenes, which are parts of the text with a consistent pattern in the four dimensions time, space, character and action. A break in these dimensions corresponds to a sc... | 0 | false | {
"start": [],
"end": [],
"text": []
} | retrieved | 5 | 2024.lrec-main.63 | 20 | [] | 0.52398 | |
ACL ethical guidelines research adherence | 2025.naacl-long.580 | 9 | ## 8 From Ethical Guidelines to Actionable Insights
To write an ethical statement, researchers can draw inspiration from resources such as frequently asked questions on ACL ethical consideration sections 9 , the ACM Code of Ethics, guidelines for NeurIPS impact statements (Ashurst et al., 2020), and governance overvie... | 1 | true | {
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"To write an ethical statement, researchers can draw inspiration from resources such as frequently asked questions on ACL ethical consideration sections 9 , the ACM Code of Ethics, guidelines for NeurIPS impact statements (Ashurst et... | retrieved | 1 | 2024.nlp4dh-1.39 | 17 | [
"To write an ethical statement, researchers can draw inspiration from resources such as frequently asked questions on ACL ethical consideration sections 9 , the ACM Code of Ethics, guidelines for NeurIPS impact statements (Ashurst et al., 2020), and governance overviews like the European Parliament's",
"When prep... | 2.107032 | |
ACL ethical guidelines research adherence | 2025.emnlp-main.1442 | 13 | ## Ethical Considerations
We affirm adherence to the ACL Rolling Review (ARR) ethical guidelines, explicitly addressing potential risks and responsible research practices. This research focuses on optimizing computational efficiency in large language models (LLMs), aimed at reducing resource usage and consequently low... | 1 | true | {
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"We affirm adherence to the ACL Rolling Review (ARR) ethical guidelines, explicitly addressing potential risks and responsible research practices. This research focuses on optimizing computational efficiency in large language models (LLMs), aimed at red... | retrieved | 2 | 2024.nlp4dh-1.39 | 17 | [
"We affirm adherence to the ACL Rolling Review (ARR) ethical guidelines, explicitly addressing potential risks and responsible research practices. This research focuses on optimizing computational efficiency in large language models (LLMs), aimed at reducing resource usage and consequently lowering environmental im... | 2.107032 | |
ACL ethical guidelines research adherence | 2023.findings-acl.0 | 42 | ## Ethics Panel
Kar¨ en Fort, Min-Yen Kan and Yulia Tsvetkov, Luciana Benotti, Mark Dredze, Pascale Fung, Dirk Hovy, Jin-Dong Kim, Malvina Nissim
Tuesday, July 11, 2023 - Room: Pier 4&5 - Time:
16:15-17:45
We present our ACL Ethics Committee's progress over the last few years. Of core interest, we will present the ... | 0 | false | {
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} | retrieved | 3 | 2024.nlp4dh-1.39 | 17 | [] | 2.107032 | |
Pearson Correlation Coefficient factual robustness faithfulness | 2022.findings-emnlp.267 | 6 |
Factual Robustness Following the definition above, we measure the factual robustness of a model via its success rate of attacks in a corpus. Given a test set D and a model with parameters θ , following Equation 4, the success rate of adversarial attack on D is calculated as:
<!-- formula-not-decoded -->
where C s (... | 1 | true | {
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"From Mix% and Incor% reported in Table 1, we can conclude that factual robustness and faithfulness have good consistency: the more factually robust the model is (lower Mix%) the better faithfulness the generated summaries exhibit (lower Incor%). Spe... | gold | -1 | 2022.findings-emnlp.267 | 6 | [
"From Mix% and Incor% reported in Table 1, we can conclude that factual robustness and faithfulness have good consistency: the more factually robust the model is (lower Mix%) the better faithfulness the generated summaries exhibit (lower Incor%). Specifically, the Pearson Correlation Coefficient and Spearman Correl... | 2.501833 | |
Pearson Correlation Coefficient factual robustness faithfulness | 2022.emnlp-main.501 | 10 | ## 4.3 Correlation between robustness metrics
To determine how results on different robustness metrics relate to each other, we compute their correlations. These correlations should indicate how much insight we can get into the overall impact of self-rationalization on a model's robustness by only looking at select me... | 1 | true | {
"start": [
881
],
"end": [
1741
],
"text": [
"n results on pairs of evaluation metrics. Each cell color is determined by absolute value of the correlation coefficient.\n\n<!-- image -->\n\nHYP and HANS, with Pearson coefficient 0.449. Furthermore, CAD and HANS, the manually annotated challenge s... | retrieved | 1 | 2022.findings-emnlp.267 | 6 | [
"n results on pairs of evaluation metrics. Each cell color is determined by absolute value of the correlation coefficient.\n\n<!-- image -->\n\nHYP and HANS, with Pearson coefficient 0.449. Furthermore, CAD and HANS, the manually annotated challenge sets, show low correlation with each other, with a Pearson coeffic... | 2.501833 | |
Pearson Correlation Coefficient factual robustness faithfulness | R09-1063 | 4 | ## 2.1 Pearson's Correlation Coefficient
The Pearson's correlation coefficient [9] is:
<!-- formula-not-decoded -->
where x is the mean of X , y the mean of Y and s x is the standard deviation of X , s y the standard deviation of Y .
The correlation coefficient measures the tendency of two variables to change in va... | 1 | true | {
"start": [
238
],
"end": [
505
],
"text": [
"The correlation coefficient measures the tendency of two variables to change in value together (i.e., to either increase or decrease). r is related with the Euclidean distance, the √ 2(1 -r ) being the Euclidean distance between the standardized versi... | retrieved | 2 | 2022.findings-emnlp.267 | 6 | [
"The correlation coefficient measures the tendency of two variables to change in value together (i.e., to either increase or decrease). r is related with the Euclidean distance, the √ 2(1 -r ) being the Euclidean distance between the standardized versions of X and Y ."
] | 2.501833 | |
Pearson Correlation Coefficient factual robustness faithfulness | 2024.inlg-main.35 | 13 | ## 6 Results and Analyses
In this section, we discuss the performance of faithfulness metrics across three distinct evaluation protocols. Subsequently, we conduct a qualitative analysis through case studies.
## 6.1 Correlation Results
The correlation results are demonstrated in Table 2. The following observations ca... | 0 | false | {
"start": [],
"end": [],
"text": []
} | retrieved | 5 | 2022.findings-emnlp.267 | 6 | [] | 2.501833 | |
LagunTest reading comprehension goal | 2020.readi-1.10 | 15 |
- Speer, R., Chin, J., Lin, A., Jewett, S., and Nathan, L. (2018). Luminosoinsight/wordfreq: v2.2, October.
- Stanovich, K. E. (1986). Matthew Effects in Reading: Some Consequences of Individual Differences in the Acquisition of Literacy. Reading Research Quarterly , 21(4):360-407.
- van Heuven, W., Mandera, P., Keul... | 0 | false | {
"start": [],
"end": [],
"text": []
} | retrieved | 1 | 2020.readi-1.10 | 14 | [] | 1.221176 | |
LagunTest reading comprehension goal | 2020.readi-1.10 | 0 | ## LagunTest : A NLP Based Application to Enhance Reading Comprehension
## Itziar Gonzalez-Dios, Kepa Bengoetxea, Amaia Aguirregoitia
University of the Basque Country (UPV/EHU) Rafael Moreno Pitxitxi , 2. 48013 Bilbao (Bizkaia) { itziar.gonzalezd, kepa.bengoetxea, amaia.aguirregoitia }
## Abstract
The ability to re... | 1 | true | {
"start": [
303
],
"end": [
1400
],
"text": [
"The ability to read and understand written texts plays an important role in education, above all in the last years of primary education. This is especially pertinent in language immersion educational programmes, where some students have low linguisti... | retrieved | 2 | 2020.readi-1.10 | 14 | [
"The ability to read and understand written texts plays an important role in education, above all in the last years of primary education. This is especially pertinent in language immersion educational programmes, where some students have low linguistic competence in the languages of instruction. In this context, ad... | 1.221176 | |
LagunTest reading comprehension goal | 2020.readi-1.10 | 8 | ## 4.2. WordCloud tab: overall representation of the text as a wordcloud
Wordclouds are visualization tools that highlight the relative frequency of words in a text. Wordclouds are very useful to quickly identify the more frequent words in the text, since they will appear bigger and bolder. This is a way to pull out t... | 0 | false | {
"start": [],
"end": [],
"text": []
} | retrieved | 5 | 2020.readi-1.10 | 14 | [] | 1.221176 | |
hope speech detection pre-trained models MuRIL XLM-RoBERTa ALBERT BERT | 2021.ltedi-1.17 | 5 | ## 3.2 Methods
Based on our previous analysis of the task description and task data, combined with the characteristics of the pre-training language model, the pretraining language model we chose in this task is the XLM-RoBERTa model.
The structure of the XLM-RoBERTa pre-training language model can be seen as a combin... | 1 | true | {
"start": [
16,
644
],
"end": [
234,
1133
],
"text": [
"Based on our previous analysis of the task description and task data, combined with the characteristics of the pre-training language model, the pretraining language model we chose in this task is the XLM-RoBERTa model.",
"Compare... | retrieved | 1 | 2021.ltedi-1.0 | 3 | [
"Based on our previous analysis of the task description and task data, combined with the characteristics of the pre-training language model, the pretraining language model we chose in this task is the XLM-RoBERTa model.",
"Compared with Bert, RoBERTa deletes the task of predicting the next sentence in the pre-tra... | 6.995731 | |
hope speech detection pre-trained models MuRIL XLM-RoBERTa ALBERT BERT | 2021.ltedi-1.17 | 3 | ## 2 Related Work
So far, many organizations and teams have invested in the identification of negative content on social media platforms and have achieved some good results (Zampieri et al., 2020; Rangel et al., 2020; Chakravarthi et al., 2020; Malmasi and Zampieri, 2017b).
However, combined with our previous analysi... | 1 | true | {
"start": [
1038
],
"end": [
1536
],
"text": [
"Chakravarthi et al. used Support vector machine (SVM), Naive Bayesian (NB), k-nearest neighbors (KNN), Decision Tree (DT), Logistic Regression (LR), and other methods on the hope speech data set to get good results (Chakravarthi, 2020). Combine thei... | retrieved | 3 | 2021.ltedi-1.0 | 3 | [
"Chakravarthi et al. used Support vector machine (SVM), Naive Bayesian (NB), k-nearest neighbors (KNN), Decision Tree (DT), Logistic Regression (LR), and other methods on the hope speech data set to get good results (Chakravarthi, 2020). Combine their work on the hope speech data set and the code-mixing in the data... | 6.995731 | |
global lexical context features properties | C10-2078 | 5 | ## 4.1 Global Lexical Context (glc)
That the lexical context might be a good indicator for the usage of an expression is obvious when one looks at examples as in (1) and (2), which suggest that literal and non-literal usages of a specific idiom co-occur with different sets of words. Nonliteral uses of break the ice (1... | 1 | true | {
"start": [
511,
1267,
1869
],
"end": [
912,
1634,
1963
],
"text": [
"t here is the global lexical context of an expression, i.e., taking into account previous and following sentences. We are specifically looking for words which are either semantically related (in a wide sense) to... | gold | -1 | C10-2078 | 5 | [
"t here is the global lexical context of an expression, i.e., taking into account previous and following sentences. We are specifically looking for words which are either semantically related (in a wide sense) to the literal or the non-\n\n2 http://maltparser.org/index.html\n\nliteral sense of the target expression... | 3.153432 | |
global lexical context features properties | C86-1028 | 4 | ## 3. Features in lexiease
As mentioned above, lexical features in a lexiease grammar are of two types: contextual and non-contextual. Contextual features specify ordering and dependency relationships among major syntactic categories ('parts of speech'), agreement and government requirements, and 'selection', semantic... | 0 | false | {
"start": [],
"end": [],
"text": []
} | retrieved | 1 | C10-2078 | 5 | [] | 3.153432 | |
global lexical context features properties | C18-1184 | 14 | ## 3.4 Ablation Study and Performance Analysis
For ablation we analyze the impact of the features and search. We also analyze the behavior of our model based on the rarity of the entities.
## 3.4.1 Feature Analysis
We study the impact of features on the local and global models. For the local model, starting with onl... | 1 | true | {
"start": [
218,
744
],
"end": [
742,
1083
],
"text": [
"We study the impact of features on the local and global models. For the local model, starting with only considering the contextual evidence as described in Section 2.2.1, we see that the performance steadily increases as we add the ... | retrieved | 2 | C10-2078 | 5 | [
"We study the impact of features on the local and global models. For the local model, starting with only considering the contextual evidence as described in Section 2.2.1, we see that the performance steadily increases as we add the prior and lexical features. As shown in tables 3(a) and (b) the prior and lexical f... | 3.153432 | |
global lexical context features properties | S10-1053 | 4 | ## 2.2 Feature Extraction
The system can extract a variety of features to be used in training and testing. A distinction can be made between local context features and global context features . Local context features are extracted from the immediate neighbours of the occurrence of the target word. One or more of the f... | 1 | true | {
"start": [
662
],
"end": [
1491
],
"text": [
"The global context features are made up of a bag-of-words representation of keywords that may be indicative for a given word to sense/translation mapping. The idea is that words are collected which have a certain power of discrimination for the speci... | retrieved | 3 | C10-2078 | 5 | [
"The global context features are made up of a bag-of-words representation of keywords that may be indicative for a given word to sense/translation mapping. The idea is that words are collected which have a certain power of discrimination for the specific target word with a specific sense, and all such words are the... | 3.153432 | |
What is HEADY? | P13-1122 | 0 | ## HEADY : News headline abstraction through event pattern clustering
Enrique Alfonseca Google Inc.
ealfonseca@google.com
Daniele Pighin Google Inc. biondo@google.com
## Abstract
This paper presents HEADY: a novel, abstractive approach for headline generation from news collections. From a web-scale corpus of Engli... | 1 | true | {
"start": [
184
],
"end": [
642
],
"text": [
"This paper presents HEADY: a novel, abstractive approach for headline generation from news collections. From a web-scale corpus of English news, we mine syntactic patterns that a Noisy-OR model generalizes into event descriptions. At inference time, w... | gold | -1 | P13-1122 | -1 | [
"This paper presents HEADY: a novel, abstractive approach for headline generation from news collections. From a web-scale corpus of English news, we mine syntactic patterns that a Noisy-OR model generalizes into event descriptions. At inference time, we query the model with the patterns observed in an unseen news c... | 1.490477 | |
What is HEADY? | N15-1014 | 7 | ## 4 Model description: headlines as bitmaps
We model headline generation as a sequence prediction task. In this manner a news article is seen as a series of observations, where each is a possible token in the document. Furthermore, each observation can be assigned to one of two categories: inheadline, or not in-headl... | 0 | false | {
"start": [],
"end": [],
"text": []
} | retrieved | 1 | P13-1122 | -1 | [] | 1.490477 | |
What is HEADY? | N15-1120 | 13 | ## 5.1.4 HEADY
HEADY produces a soft-clustering from a generative model, and expects the maximum number of clusters to be provided beforehand. The model then tries to approximate this number. In our experiments, 5,496 clusters were finally generated. One weak point of HEADY, mentioned above, is that lowfrequency patte... | 1 | true | {
"start": [
16
],
"end": [
505
],
"text": [
"HEADY produces a soft-clustering from a generative model, and expects the maximum number of clusters to be provided beforehand. The model then tries to approximate this number. In our experiments, 5,496 clusters were finally generated. One weak point o... | retrieved | 2 | P13-1122 | -1 | [
"HEADY produces a soft-clustering from a generative model, and expects the maximum number of clusters to be provided beforehand. The model then tries to approximate this number. In our experiments, 5,496 clusters were finally generated. One weak point of HEADY, mentioned above, is that lowfrequency patterns do not ... | 1.490477 | |
What is HEADY? | C88-1004 | 9 | ## 4.1.HEAD Feature List [HFL]:
It contains information stating which f-v-pairs are considered to be members of the set HEAD. The decision of which f-v-pairm must be HF£D members is crucial to the model. In a first approach, we will adopt a pragmatical criterion. This means that we will include as HEAD features those ... | 0 | false | {
"start": [],
"end": [],
"text": []
} | retrieved | 3 | P13-1122 | -1 | [] | 1.490477 | |
What is HEADY? | 2023.acl-long.475 | 4 | ## 3 Methodology
## 3.1 Notations
Supposing the PLM consists of L encoder layers, and MHA has H attention heads and the corresponding weights 3 are W Q h , W K h , W V h ∈ R D × D ′ , h ∈ { 1 , 2 , · · · , H } , where D refers to the hidden representation dimensions and D ′ = D H . Let the weight set of the h -th hea... | 0 | false | {
"start": [],
"end": [],
"text": []
} | retrieved | 5 | P13-1122 | -1 | [] | 1.490477 | |
sarcasm detection training data fields | 2020.figlang-1.15 | 3 | ## 3 Dataset Description
The data 1 we used for model building is taken from sarcasm detection shared task of the Sec-
1 https://competitions.codalab.org/competitions/22247
ond Workshop on Figurative Language Processing (FigLang2020). There are two types of data provided by the organizers: 1. Twitter dataset and 2. ... | 1 | true | {
"start": [
336
],
"end": [
446
],
"text": [
"Training data contains the fields 'label', 'response' and 'context' and are described as shown in the Table 1."
]
} | gold | -1 | 2020.figlang-1.15 | 3 | [
"Training data contains the fields 'label', 'response' and 'context' and are described as shown in the Table 1."
] | 1.126537 | |
sarcasm detection training data fields | 2020.figlang-1.1 | 5 | ## 3.1.2 Twitter Training Dataset
For the Twitter dataset, we have relied upon the annotations that users assign to their tweets using hashtags. The sarcastic tweets were collected using hashtags: #sarcasm and #sarcastic . As nonsarcastic utterances, we consider sentiment tweets, i.e., we adopt the methodology propose... | 0 | false | {
"start": [],
"end": [],
"text": []
} | retrieved | 1 | 2020.figlang-1.15 | 3 | [] | 1.126537 | |
sarcasm detection training data fields | 2020.figlang-1.15 | 2 | ## 2 Related work
Sarcasm is a form of figurative language where the meaning of a sentence does not hold and the interpretation is quite contrary. A quick survey about sarcasm detection and some of the earlier approaches is compiled by Joshi et al. (2017).
The problem of sarcasm detection is targeted in
Table 1: Fie... | 1 | true | {
"start": [
308
],
"end": [
580
],
"text": [
"Table 1: Fields used in the training data\n\n| Field | Field Description |\n|------------------------|------------------------------------------------------------------------|\n| label response context | SARCASM or NOT SARCASM Tweet or a Reddit post O... | retrieved | 2 | 2020.figlang-1.15 | 3 | [
"Table 1: Fields used in the training data\n\n| Field | Field Description |\n|------------------------|------------------------------------------------------------------------|\n| label response context | SARCASM or NOT SARCASM Tweet or a Reddit post Ordered list of dialogue |"
] | 1.126537 | |
sarcasm detection training data fields | 2020.clicit-1.22 | 4 | ## 4 Data Set
We use the Twitter Corpus from the CodaLab shared task on sarcasm detection (Ghosh et al., 2020). The training data consists of 2,500 tweets labeled 'SARCASM' and 2,500 tweets labeled 'NON SARCASM', the balanced test data consists of an additional 1,800 labeled tweets. Ghosh et al. (2020), this is a self... | 0 | false | {
"start": [],
"end": [],
"text": []
} | retrieved | 3 | 2020.figlang-1.15 | 3 | [] | 1.126537 |
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