The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
QuAC: Question Answering in Context
Open accessAbstract
We present QuAC, a dataset for Question Answering in Context that contains 14K information-seeking QA dialogs (100K questions in total).The dialogs involve two crowd workers: (1) a student who poses a sequence of freeform questions to learn as much as possible about a hidden Wikipedia text, and (2) a teacher who answers the questions by providing short excerpts from the text.QuAC introduces challenges not found in existing machine comprehension datasets: its questions are often more open-ended, unanswerable, or only meaningful within the dialog context, as we show in a detailed qualitative evaluation.We also report results for a number of reference models, including a recently state-ofthe-art reading comprehension architecture extended to model dialog context.Our best model underperforms humans by 20 F1, suggesting that there is significant room for future work on this data.Dataset, baseline, and leaderboard available at http://quac.ai.How was perversion handled?How long was he there?How popular did she become?How did Mark Felt contact Woodword?How did the meeting go?How did it do on the charts?When was she born?When was it founded?When was the breakup?
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“Most datasets that are currently available for training question generation systems are crowd-sourced [42, 47, 84, 92, 114, 115, 119, 120, 122, 123], which largely impacts the quality of generated questions.”
From Automatic question generation: a review of methodologies, datasets, evaluation metrics, and applications (2023) · cites this paper as [114]
In the citing paper
“For question answering (QA), on SQuADv2 [97], QuAC [24], and many other datasets, fine-tuned models have superior performance, while on CoQA [98], LLMs perform as well as fine-tuned models [25].”
From Harnessing the power of llms in practice: a survey on chatGPT and beyond (2024) · cites this paper as [24]
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Cites
Deep Contextualized Word Representations
Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1202 · Cited by 11,983 · cited 2×
“BiDAF++ We use a re-implementation of a top-performing SQuAD model (Peters et al., 2018) that augments bidirectional attention flow (Seo et al., 2016, BiDAF) with self-attention (Clark and Gardner, 2018) and contextualized embeddings.”
MethodSelf-citationSQuAD: 100,000+ Questions for Machine Comprehension of Text
Rajpurkar, P., Zhang, J., Lopyrev, K., & Liang, P. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1264 · Cited by 6,208 · cited 2×
“Reading Comprehension Our work builds on span based reading comprehension (Rajpurkar et al., 2016; Joshi et al., 2017; Trischler et al., 2016), while also incorporating innovations such as curating questions independently of supporting text to reduce trivial lexical overlap (Joshi et al., 2017;…”
BackgroundSelf-citationYago: a core of semantic knowledge
Suchanek, F. M., Kasneci, G., & Weikum, G. (2007) · Proceedings of the 16th international conference on World Wide Web · doi:10.1145/1242572.1242667 · Cited by 2,512
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
Bajaj, P., Campos, D., Craswell, N., Deng, L., Gao, J., Liu, X., Majumder, R., McNamara, A., Mitra, B., Nguyen, T., et al. (2016) · arXiv · doi:10.48550/arxiv.1611.09268 · Cited by 2,473
Know What You Don’t Know: Unanswerable Questions for SQuAD
Rajpurkar, P., Jia, R., & Liang, P. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · doi:10.18653/v1/p18-2124 · Cited by 2,240
Self-citationSupervised Learning of Universal Sentence Representations from Natural Language Inference Data
Conneau, A., Kiela, D., Schwenk, H., Barrault, L., & Bordes, A. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1070 · Cited by 2,049 · cited 1×
“Pretrained InferSent To test the importance of lexical matching in our dataset, we output the sentence in s whose pretrained InferSent representation (Conneau et al., 2017) has the highest cosine similarity to that of the question.”
MethodBidirectional Attention Flow for Machine ComprehensionPreprint
Seo, M. J., Kembhavi, A., Farhadi, A., & Hajishirzi, H. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.01603 · Cited by 1,698 · cited 1×
“BiDAF++ We use a re-implementation of a top-performing SQuAD model (Peters et al., 2018) that augments bidirectional attention flow (Seo et al., 2016, BiDAF) with self-attention (Clark and Gardner, 2018) and contextualized embeddings.”
MethodTriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
Joshi, M., Choi, E., Weld, D., & Zettlemoyer, L. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1147 · Cited by 1,690 · cited 8×
“adopted in other reading comprehension datasets such as SQuAD, TriviaQA (Joshi et al., 2017), and NewsQA (Trischler et al.”
BackgroundMethodInfluentialSelf-citationDeep Reinforcement Learning for Dialogue Generation
Li, J., Monroe, W., Ritter, A., Jurafsky, D., Galley, M., & Gao, J. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1127 · Cited by 1,393 · cited 1×
“19 Dialog fits into an increasing interest in open domain dialog, mostly studied in the context of social chit-chat (Li et al., 2016; Ritter et al., 2011; Fang et al., 2017; Ghazvininejad et al., 2018).”
BackgroundAllenNLP: A Deep Semantic Natural Language Processing Platform
Gardner, M., Grus, J., Neumann, M., Tafjord, O., Dasigi, P., Liu, N. F., Peters, M. E., Schmitz, M., & Zettlemoyer, L. (2018) · Workshop for NLP Open Source Software (NLP-OSS) · doi:10.18653/v1/w18-2501 · Cited by 1,376 · cited 1×
“The AllenNLP (Gardner et al., 2017) implementation we use reaches 82.”
MethodSelf-citation
Flagged first, then by citations
Research works printed but not linked: 3
- Hao Fang, Hao Cheng, Elizabeth Clark, Ariel Holtz- man, Maarten Sap, Mari Ostendorf, Yejin Choi, and Noah A Smith. 2017. Sounding board-university of washingtons alexa prize submission. Alexa Prize Proceedings.
- Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018. Deep contextualized word rep- resentations. Conference of the North American Chapter of the Association for Computational Lin- guistics.
- Marzieh Saeidi, Max Bartolo, Patrick Lewis, Sameer Signh, Tim Rocktschel, Mike Sheldon, Guillaume Bouchard, and Sebastian Riedel. 2018. Interpreta- tion of natural language rules in conversational ma- chine reading. In Proceedings of Empirical Methods in Natural Language Processing.
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Items 701–704 of 704
DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs
Dua, D., Wang, Y., Dasigi, P., Stanovsky, G., Singh, S., & Gardner, M. (2019) · arXiv · doi:10.48550/arxiv.1903.00161 · Cited by 238
“All of these datasets throw in additional complexities to the reading comprehension challenge, around tracking conversational state (Reddy et al., 2019; Choi et al., 2018), requiring passage retrieval (Joshi et al.”
BackgroundThomason, J., Murray, M., Cakmak, M., & Zettlemoyer, L. (2019) · arXiv · doi:10.48550/arxiv.1907.04957 · Cited by 173
“Recently, this paradigm was extended to two-sided dialogs via human-human, question-answer pairs about a document [21, 22, 23].”
BackgroundSelf-citationConversational AI Systems for Social Good: Opportunities and Challenges
Qi, P., Huang, J., Wu, Y., He, X., & Zhou, B. (2021) · arXiv · doi:10.48550/arxiv.2105.06457 · Cited by 3
“Some take the familiar form of a ConvAI sys-tem assisting human users ( e.g. , knowledge-based ConvAI, including question answering systems (Choi et al. 2018)), while others focus more on conversational skills that featured in collaborative or competitive peer conversations (He et al. 2017, 2018).”
BackgroundA Dataset of Information-Seeking Questions and Answers Anchored in Research Papers
Dasigi, P., Lo, K., Beltagy, I., Cohan, A., Smith, N. A., & Gardner, M. (2021) · arXiv · doi:10.48550/arxiv.2105.03011
“Examples of such datasets include WikiQA (Yang et al., 2015), NewsQA (Trischler et al., 2017), MsMarco (Campos et al., 2016), QuAC (Choi et al., 2018), Natural Questions (Kwiatkowski et al., 2019), TyDiQA (Clark et al., 2020), and IIRC (Ferguson et al., 2020).”
BackgroundMethod
Metadata
- Authors
- Choi, E., He, H., Iyyer, M., Yatskar, M., Yih, W.-T., Choi, Y., Liang, P., & Zettlemoyer, L.
- Affiliations
- University of Washington (US)
- Venue
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Published
- 2018
- Pages
- 2174-2184
- OpenAlex
- W2888302696
- Cited by
- 714
- References
- 28
- Citations by year
- 2026: 20 · 2025: 41 · 2024: 84 · 2023: 105 · 2022: 101 · 2021: 150 · 2020: 115 · 2019: 88 · 2018: 10
- FWCI
- 36.11
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- Funders
- Allen Institute for Artificial Intelligence
- Defense Advanced Research Projects Agency
- Army Research Office (W911NF-15-1-, W911NF)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Keywords
- information-seeking questions, open-ended questions, unanswerable questions, reading comprehension
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