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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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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.
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Items 401–500 of 704
Incremental BERT with commonsense representations for multi-choice reading comprehension
Li, R., Wang, L., Jiang, Z., Liu, D., Zhao, M., & Lu, X. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11197-0 · Cited by 7
“, cloze-style [8, 9], extractive/abstractive answer [6, 15, 16, 22, 25], multi-choice [17], conversational QA [3, 26], multi-hop [38, 43], and whether external knowledge is needed [4, 13, 19, 30, 46].”
BackgroundAsking the Right Question: Inferring Advice-Seeking Intentions from Personal NarrativesPreprint
Fu, L., Chang, J. P., & Danescu-Niculescu-Mizil, C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.01587 · Cited by 5
“Conversational QA datasets (Choi et al., 2018; Reddy et al., 2019) have been introduced to help develop systems with such capability.”
BackgroundInterpretation of Natural Language Rules in Conversational Machine ReadingPreprint
Saeidi, M., Bartolo, M., Lewis, P., Singh, S., Rocktäschel, T., Sheldon, M., Bouchard, G., & Riedel, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.01494 · Cited by 30
“In parallel to our work, both Choi et al. (2018) and Reddy et al. (2018) have to began to investigate QA dialogs with background text.”
BackgroundLarge Scale Question Answering using Tourism DataPreprint
Contractor, D., Shah, K., Partap, A., Mausam, & Singla, P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.03527 · Cited by 9
“, 2019) Passages on topics Free text 858 Y N Dialog QA - QuAC (Choi et al., 2018) Wikipedia passages Span 401 Y N Dialog QA - CoQA (Reddy, Chen, and Manning, 2018) Passages on topics Free text 271 Y N Our Dataset Reviews Entity (doc.”
BackgroundA Pilot Study on Multiple Choice Machine Reading Comprehension for Vietnamese TextsPreprint
Nguyen, K. V., Tran, K. V., Luu, S. T., Nguyen, A. G.-T., & Nguyen, N. L.-T. (2020) · arXiv (Cornell University) · Cited by 5
How Can BERT Help Lexical Semantics Tasks?Preprint
Wang, Y., Cui, L., & Zhang, Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.02929 · Cited by 12
“, 2014), giving significantly improved results in a wide range of NLP tasks, including question answering (Choi et al., 2018), reading comprehension (Xu et al.”
Robust Dialogue Utterance Rewriting as Sequence TaggingPreprint
Hao, J., Song, L., Wang, L., Xu, K., Tu, Z., & Yu, D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.14535 · Cited by 5
“Recent years have witnessed increasing attention in conversation-based tasks, such as conversational question answering (Choi et al., 2018; Reddy et al., 2019; Sun et al., 2019) and dialogue response generation (Li et al., 2017; Zhang et al., 2018; Wu et al., 2019; Zhou et al., 2020), mainly due to…”
Conversations with Search Engines: SERP-based Conversational Response GenerationPreprint
Ren, P., Chen, Z., Ren, Z., Kanoulas, E., Monz, C., & de Rijke, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.14162 · Cited by 5
“l datasets on search, chitchat and question answer. Dataset Multiple turns Complex query Open domain Conversational response CAsT [12] ✓ ✓ ✓ ✗ Holl-e [23], WoW [13] ✓ ✓ ✗ ✓ MS MARCO [24] ✗ ✓ ✓ ? QuAC [10], CoQA [30] ✓ ✗ ✗ ✗ SaaC (this paper) ✓ ✓ ✓ ✓ conversational queries by introducing coherent transitions, e.g., cor…”
InfluentialWizard of Search Engine: Access to Information Through Conversations with Search EnginesPreprint
Ren, P., Liu, Z., Song, X., Tian, H., Chen, Z., Ren, Z., & de Rijke, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.08301 · Cited by 9
“, knowledge grounded conversations [14, 22], conversational question answering [8, 10, 29, 32].”
BackgroundImproving Conversational Passage Re-ranking with View Ensemble
Ju, J.-H., Lin, S.-C., Tsai, M.-F., & Wang, C. (2023) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3539618.3592002 · Cited by 4
“ambiguity in conversational queries, making it one of the most distinctive challenges for conversational AI systems [1, 3, 27, 31].”
BackgroundMethodInfluentialWang, H., Hu, K., & Gao, L. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10887668 · Cited by 4
“Additionally, the scope of QA has evolved from simple factoid-based questions [2] to more complex, context-dependent questions [3] that require deep understanding and reasoning over extensive documents.”
DialDoc 2021 Shared Task: Goal-Oriented Document-grounded Dialogue Modeling
Feng, S. (2021) · doi:10.18653/v1/2021.dialdoc-1.1 · Cited by 4
“There have been significant individual research threads that show promises in handling heterogeneous knowledge embedded in the documents (Talmor et al., 2021), including (1) unstructured content such as text passages (CoQA (Reddy et al., 2019), QuAC (Choi et al., 2018), ShARC (Saeidi et al., 2018), DoQA (Campos et al.,…”
BackgroundSmoothing Dialogue States for Open Conversational Machine Reading
Zhang, Z., Ouyang, S., Zhao, H., Utiyama, M., & Sumita, E. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.299 · Cited by 4
“Most of the current conversation-based reading comprehension tasks are formed as either spanbased QA (Reddy et al., 2019; Choi et al., 2018) or multi-choice tasks (Sun et al.”
BackgroundParagraph Similarity Matches for Generating Multiple-choice Test Items
Maslak, H., & Mitkov, R. (2021) · Student Research Workshop .../Proceedings of the Student Research Workshop ... · doi:10.26615/issn.2603-2821.2021_015 · Cited by 4
“Such question answering datasets as The Natural Questions corpus (Kwiatkowski et al., 2019), TriviaQA (Joshi et al., 2017), HotpotQA (Yang et al., 2018), QuAC (Choi et al., 2018), QASPER (Dasigi et al., 2021), CaseHOLD (Zheng et al., 2021), OpenBookQA (Mihaylov et al., 2018), SQuAD (Rajpurkar et al., 2016), and MultiR…”
MethodSyntax-Aware Multi-Spans Generation for Reading Comprehension
Zhang, Z., Zhang, Y., & Zhao, H. (2021) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2021.3138679 · Cited by 10
MeetingQA: Extractive Question-Answering on Meeting Transcripts
Prasad, A., Bui, T., Yoon, S., Deilamsalehy, H., Dernoncourt, F., & Bansal, M. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.837 · Cited by 8
“Note that our work is different from conversational QA datasets that consist of a sequence of questions and answers simulating a conversation grounded in a short paragraph (Choi et al., 2018; Reddy et al., 2019; Campos et al., 2020).”
BackgroundToward Connecting Speech Acts and Search Actions in Conversational Search Tasks
Ghosh, S., Ghosh, S., & Shah, C. S. (2023) · ACM/IEEE Joint Conference on Digital Libraries · doi:10.1109/jcdl57899.2023.00027 · Cited by 6
“Deep neural networks have also been used to answer complex questions [45], predict the success of dialogues [57], improve contextual awareness [110], reformulate multiturn questions [23, 32], topic propagation [63], present exploratory search results as interactive stories [101] and result presentation of structured d…”
BackgroundTrippas, J. R. (2024) · ACM eBooks · doi:10.1145/3674127.3674135 · Cited by 5
Chatty Goose: A Python Framework for Conversational Search
Zhang, E., Lin, S.-C., Yang, J.-H., Pradeep, R., Nogueira, R. F., & Lin, J. (2021) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3404835.3462782 · Cited by 8
“Substantial progress has been made in recent years, driven by datasets such as CoQA [15] and QuAC [3].”
MethodImproving Low resource Reading Comprehension via Cross lingual Transposition Rethinking
Wu, G., Xu, B., Qin, Y., Wang, W., & Wang, G. (2021) · International Joint Conference on Knowledge Graphs (IJCKG) · doi:10.1145/3502223.3502234 · Cited by 6
Technical Report on Shared Task in DialDoc21
Li, J., Li, M., Ma, L., Zhang, W.-N., & Liu, T. (2021) · doi:10.18653/v1/2021.dialdoc-1.7 · Cited by 4
“The CQA (such as CoQA (Reddy et al., 2019), QuAC (Choi et al., 2018) and DoQA (Campos et al., 2020)) task is also based on background document, which aims to understand a text passage and answering a series of interconnected questions that appear in a conversation.”
MethodSurvey of Machine Reading Comprehension Models and its Evaluation Metrics
Therasa, M., & Mathivanan, G. (2022) · International Conference on Computing Methodologies and Communication (ICCMC) · doi:10.1109/iccmc53470.2022.9754070 · Cited by 4
Zeng, H., Wei, B., Liu, J., & Fu, W. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.500 · Cited by 5
“, 2019), QuAC (Choi et al., 2018), and DoQA (Campos et al.”
BackgroundMethodInfluentialTab-CQA: A Tabular Conversational Question Answering Dataset on Financial Reports
Liu, C., Li, J., & Xiong, D. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-industry.20 · Cited by 7
“Previous CQA datasets have been constructed either for text span extracting tasks (Choi et al., 2018; Reddy et al., 2019; Campos et al., 2020; Saeidi et al., 2018) or SQL-style table search tasks (Iyyer et al., 2017; Yu et al., 2019b).”
MethodLapchaicharoenkit, T., & Vateekul, P. (2020) · International Conference on Computer and Communications Management (ICCCM) · doi:10.1145/3411174.3411184 · Cited by 4
“Previous researches deal with multiclass questions by appending special token to the context passage [4] or adding more prediction tasks to the same block of the model [6-8].”
BackgroundMethodInfluentialLevannoza, R., Latif, R. F., Nurkafianti, S. I., Oktriono, K., Devina, D., Wiharja, C. K., & Cenggoro, T. W. (2020) · International Conference on Information Management and Technology (ICIMTech) · doi:10.1109/icimtech50083.2020.9211162 · Cited by 4
“Since then, the popular question answering datasets were collected using crowdsourcing [8]– [11].”
BackgroundToward Conversational Query Reformulation.
Kiesel, J., Cai, X., Baff, R. E., Stein, B., & Hagen, M. (2021) · Cited by 4
Schlegel, V., Nenadić, G., & Batista-Navarro, R. (2022) · Natural Language Engineering · doi:10.1017/s1351324922000171 · Cited by 27
A Dataset for Sentence Retrieval for Open-Ended Dialogues
Harel, I., Taitelbaum, H., Szpektor, I., & Kurland, O. (2022) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3477495.3531727 · Cited by 6
“Prominent conversational QA datasets include CoQA [35], DoQA [2] and QuAC [3].”
BackgroundMethodDisentangling Indirect Answers to Yes-No Questions in Real Conversations
Sanagavarapu, K. C., Singaraju, J. P., Kakileti, A., Kaza, A., Mathews, A., Li, H., Brito, N., & Blanco, E. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.345 · Cited by 4
“Two recent corpora consist of dialogues generated by crowdworkers on demand after being given some text to talk about: QuAC (Choi et al., 2018) and CoQA (Reddy et al., 2019).”
BackgroundDiscourse Markers in Interaction
Li, R., Allein, L., Sileo, D., & Moens, M. (2022) · De Gruyter eBooks · doi:10.1515/9783110790351 · Cited by 4
“2018; Allein, Leeuwenberg, and Moens 2020), locate the correct answer to questions (Choi et al. 2018; Cartuyvels, Spinks, and Moens 2020) or even detect offensive or incorrect information (Ghadery and Moens 2020; Allein, Augenstein, and Moens 2021).”
BackgroundTT-Net: Topic Transfer-Based Neural Network for Conversational Reading Comprehension
Gu, Y., Gui, X., & Li, D. (2019) · IEEE Access · doi:10.1109/access.2019.2936011 · Cited by 4
Representing Words in Vector Space and Beyond
Wang, B., Buccio, E. D., & Melucci, M. (2019) · STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics & Health · doi:10.1007/978-3-030-25913-6_5 · Cited by 4
FastFusionNet: New State-of-the-Art for DAWNBench SQuADPreprint
Wu, F., Li, B., Wang, L., Lao, N., Blitzer, J. C., & Weinberger, K. Q. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.11291 · Cited by 5
“An array of reading comprehension datasets have been released in the past years including WikiQA [37], SQuAD [25], SQuAD v2 [26], TriviaQA [13], SearchQA [8], NarrativeQA [17], CoQA [27], QuAC [4], and Natural Questions [18].”
Generating More Interesting Responses in Neural Conversation Models with Distributional ConstraintsPreprint
Baheti, A., Ritter, A., Li, J., & Dolan, B. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.01215 · Cited by 4
Tang, H., Liu, J., Li, H., Yu, H., Wu, H. R., & Wang, H. (2020) · arXiv (Cornell University) · Cited by 3
A Graph-guided Multi-round Retrieval Method for Conversational Open-domain Question AnsweringPreprint
Li, Y., Li, W., & Nie, L. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.08443 · Cited by 12
“Following the previouswork [5, 21], we employed the word-level F1 and the human equivalence score (HEQ), which are two metrics provided by the QuAC challenge [5] to evaluate the Conversational QA systems.”
InfluentialVANiLLa : Verbalized Answers in Natural Language at Large ScalePreprint
Biswas, D., Dubey, M., Rony, M. R. A. H., & Lehmann, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.11407 · Cited by 7
“Furthermore, datasets are also adapted into conversation or dialog format with multiple utterances such as, QuAC [4], CoQA [20], CSQA [21], SQA [13].”
Automatic question answering for multiple stakeholders, the epidemic question answering dataset
Goodwin, T. R., Demner‐Fushman, D., Lo, K., Wang, L. L., Dang, H. T., & Soboroff, I. M. (2022) · Scientific Data · doi:10.1038/s41597-022-01533-w · Cited by 4
Improving Production Management and Control with Intelligent Chatbot Services
Mashkouk, R., Novitsky, V., Korzanova, A., Kornev, D., & Myshenkov, K. S. (2024) · International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE) · doi:10.1109/reepe60449.2024.10479952 · Cited by 3
“…human language by machines, exploiting pre-trained language models [7, 8, 9, 10], memory-based architectures, and new conversational datasets [11, 12, 13, 14, 15], which represents a significant evolution from the early stages of dialogue system development, that relied on rule-based systems [16].”
BackgroundText Mining of CVD Synthesis Recipes for 2D Materials
Lu, A., Chen, R., Yao, A., Chen, M.-C., Park, J. H., Zhang, T., Zheng, X., Mao, N., Wang, J., Wang, Z., et al. (2026) · Advanced Materials · doi:10.1002/adma.202509132 · Cited by 3
R3 : Refined Retriever-Reader pipeline for Multidoc2dial
Bansal, S., Tripathi, S., Agarwal, S. D., Gururaja, S., Veerubhotla, A. S., Dutt, R., Mitamura, T., & Nyberg, E. (2022) · DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering (DialDoc) · doi:10.18653/v1/2022.dialdoc-1.17 · Cited by 5
“In contrast to tasks like its predecessor, Doc2Dial (Feng et al., 2020a), and related tasks like QuAC (Choi et al., 2018a), ShARC (Saeidi et al.)”
BackgroundMethodFrBMedQA: the first French biomedical question answering dataset
Kaddari, Z., & Bouchentouf, T. (2022) · IAES International Journal of Artificial Intelligence · doi:10.11591/ijai.v11.i4.pp1588-1595 · Cited by 3
Layout-Aware Information Extraction for Document-Grounded Dialogue
Zhang, Z., Yu, B., Yu, H., Liu, T., Fu, C., Li, J., Tang, C., Sun, J., & Li, Y. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3548765 · Cited by 8
“The task of reading documents and responding to queries has been the trigger of recent research advances [2, 6, 29].”
BackgroundS2M: Converting Single-Turn to Multi-Turn Datasets for Conversational Question Answering
Li, B., Zhang, S., Zhang, W., Chen, Y.-C., Yang, C., Hu, S., Xu, T., Liu, S., & Li, J. (2023) · Frontiers in artificial intelligence and applications · doi:10.3233/faia230413 · Cited by 3
“The task of conversational question answering, which requires machines to answer questions through reading and understanding a given context and history Question-Answer pairs, has been a rapidly growing area in natural language understanding [3, 23, 25, 26].”
BackgroundMethodInfluentialq2d: Turning Questions into Dialogs to Teach Models How to Search
Bitton, Y., Cohen-Ganor, S., Hakimi, I., Lewenberg, Y., Aharoni, R., & Weinreb, E. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.843 · Cited by 7
Łajewska, W., Balog, K., Spina, D., & Trippas, J. R. (2024) · Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region (SIGIR-AP) · doi:10.1145/3673791.3698409 · Cited by 5
“…retrieved passages faces additional challenges related to temporal considerations [7], biased queries [2, 22], source subjectivity, unanswerability [8, 33, 35, 37], the lack of expert knowledge , and additional issues related to text aggregation that may introduce hallucinations and factual errors…”
BackgroundInvestigating legal question generation using large language models
Deroy, A., Ghosh, K., & Ghosh, S. (2025) · Artificial Intelligence and Law · doi:10.1007/s10506-025-09452-y · Cited by 5
Gody, R., Abdelghaffar, M., Jabreel, M., & Tawfik, A. Y. (2025) · IEEE Conference on Artificial Intelligence (CAI) · doi:10.1109/cai64502.2025.00042 · Cited by 3
“To enhance the representation of information-seeking conversations, we use the Question Answering in Context (QuAC) dataset [29] and the Conversational Curiosity Corpus [30], as well as a sampled subset of the Natural Questions dataset [31].”
Summary-Oriented Question Generation for Informational Queries
Yin, X., Zhou, L., Small, K., & May, J. (2021) · doi:10.18653/v1/2021.dialdoc-1.11 · Cited by 4
“However, despite technological advancements that support more sophisticated questions (Yang et al., 2018; Joshi et al., 2017; Choi et al., 2018; Reddy et al., 2019), many consumers of QA technology in practice tend to ask simple factoid questions when engaging with these systems.”
BackgroundAksu, T., Liu, Z., Kan, M., & Chen, N. F. (2021) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/2021.sigdial-1.14 · Cited by 3
“In contrast, CoQa and QuAC do employ two-party dialogue; however, their task is to conversationally answer questions about a passage, diverging from our task definition (Reddy et al., 2020; Choi et al., 2018).”
BackgroundA Dataset of Argumentative Dialogues on Scientific Papers
Ruggeri, F., Mesgar, M., & Gurevych, I. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.425 · Cited by 10
“, 2019), QuAC (Choi et al., 2018) and Doc2Dial (Feng et al.”
MethodResultInfluentialLi, J., Yu, M., Meng, F., Lin, Z., Fu, P., Wang, W., & Zhou, J. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.306 · Cited by 3
Learning to Retrieve Engaging Follow-Up Queries
Richardson, C., Kar, S., Kumar, A., Ramachandran, A., Raeesy, Z., Khan, O., & Sethy, A. (2023) · Findings of the Association for Computational Linguistics: EACL · doi:10.18653/v1/2023.findings-eacl.149 · Cited by 3
Sheng, R., Yang, L., Li, H., Luo, Y., Xu, Z., Zhou, Z., Gotz, D., & Qu, H. (2023) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3586182.3615785 · Cited by 3
“Second, we design the prompt to generate questions that start with “what” , “how” , “why” , and so on based on the question taxonomy concluded by [2].”
MethodStructure-Aware Dialogue Modeling Methods for Conversational Semantic Role Labeling
Wu, H., Xu, K., & Song, L. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3331576 · Cited by 3
“generation [2], [3], [4], task-oriented dialogue modeling [5], [6] and conversational question answering [7], [8].”
BackgroundCausal Document-Grounded Dialogue Pre-training
Zhao, Y., Yu, B., Li, B., Yu, H., Li, J., Wang, C., Huang, F., Li, Y., & Zhang, N. L. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.443 · Cited by 10
Mulla, N., & Gharpure, P. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2023.12.041 · Cited by 4
A Token-based transition-aware joint framework for multi-span question answering
Luo, Z., Zhang, Y., & Luo, S. (2024) · Information Processing & Management · doi:10.1016/j.ipm.2024.103678 · Cited by 5
Łajewska, W., & Balog, K. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-56063-7_25 · Cited by 5
DC-Graph: a chunk optimization model based on document classification and graph learning
Zhou, J., Zhang, G., Alfarraj, O., Tolba, A. K. R., Li, X., & Zhang, H. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10771-w · Cited by 3
“CoQA and QuAC are dialogue-based machine reading comprehension datasets, where each sample consists of multiple questions and answers on a given context document.”
InfluentialConversational Question Answering Systems: A Comprehensive Literature Review
Vadhavana, V., Patel, K., Patel, B., Patel, B., Parmar, N., & Patel, V. (2024) · International Conference on Inventive Computation Technologies (ICICT) · doi:10.1109/icict60155.2024.10544989 · Cited by 3
A Model-Based Evaluation Metric for Question Answering Systems
Bakır, D., Aktaş, M. S., & Yildiz, B. (2024) · International Journal of Software Engineering and Knowledge Engineering · doi:10.1142/s0218194025500032 · Cited by 3
Open-Domain Factoid Question-Answering in Urdu: Data and Methods
Shakeel, M., & Nawab, R. M. A. (2025) · IEEE Access · doi:10.1109/access.2025.3540939 · Cited by 3
Descriptor: Open-Domain Long-Form Context-Aware Question-Answering Dataset (DragonVerseQA)
Lahiri, A. K., & Hu, Q. (2025) · IEEE data descriptions. · doi:10.1109/ieeedata.2025.3562173 · Cited by 3
“Some other long-form QA datasets worth mentioning in this regard are conversational QA datasets such as QuAC [15] and CoQA [16] that emphasize dialogue-based context retention, differing from DragonVerseQA, which provides rich narrative contexts for TV series episodes.”
Self-evaluation of LLMs on challenging LLM-generated STEM MCQs
Soliman, G., Zaki, H., & Lotfy, M. (2025) · Neural Computing and Applications · doi:10.1007/s00521-025-11164-y · Cited by 4
QuesBELM: A BERT based Ensemble Language Model for Natural Questions
Pranesh, R. R., Shekhar, A., & Pallavi, S. (2020) · International Conference on Computing, Communication and Security (ICCCS) · doi:10.1109/icccs49678.2020.9277176 · Cited by 3
Efficient natural language classification algorithm for detecting duplicate unsupervised features
Altaf, S., Iqbal, S., & Soomro, M. W. (2021) · Informatics and Automation · doi:10.15622/ia.2021.3.5 · Cited by 3
Khosla, S., Lovelace, J., Dutt, R., & Pratapa, A. (2021) · doi:10.18653/v1/2021.dialdoc-1.13 · Cited by 3
“However, combining CoQA and QuAC during pre-training does not seem to help with the performance on validation or testdev split.”
BackgroundInfluentialA Knowledge storage and semantic space alignment Method for Multi-documents dialogue generation
Zhu, M., Li, B., Weng, Y., & Xia, F. (2022) · DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering (DialDoc) · doi:10.18653/v1/2022.dialdoc-1.14 · Cited by 3
“Prior work typically formulates the task as a machine reading comprehension task assuming the associated document or text snippet is given, such as QuAC (Choi et al., 2018), ShARC (Saeidi et al., 2018), CoQA (Reddy et al., 2019), OR-QuAC (Qu et al., 2020) and Doc2Dial (Feng et al., 2020).”
BackgroundCOVID-19-Related Scientific Literature Exploration: Short Survey and Comparative Study
Bahaj, A., Lhazmir, S., Ghogho, M., & Benbrahim, H. (2022) · Biology · doi:10.3390/biology11081221 · Cited by 3
Sliding-Bert: Striding Towards Conversational Machine Comprehension in Long Contex
Zhang, L., Wang, W., Yu, K., Huang, J., Lyu, Q., Xue, H., & Hetang, C. (2023) · Advances in Artificial Intelligence and Machine Learning · doi:10.54364/aaiml.2023.1178 · Cited by 4
Automatic Numerical Question Answering on Table using BERT-GNN
Bagwe, R., & George, K. (2020) · IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) · doi:10.1109/uemcon51285.2020.9298028 · Cited by 3
“In the past few years, progress has been made in this community to build an automatic question-answering (QA) mechanisms [6], [7], [8].”
BackgroundTesting QA Systems’ ability in Processing Synonym Commonsense Knowledge
Sigdel, B., Lin, G., Miao, Y., & Ahmed, K. E. U. (2020) · International Conference on Information Visualisation (IV) · doi:10.1109/iv51561.2020.00059 · Cited by 3
Question Classification of CoQA (QCoC) Dataset
Lokman, A. S., Ameedeen, M. A., & Ghani, N. A. (2021) · International Conference on Software Engineering and Computer Systems · doi:10.1109/icsecs52883.2021.00123 · Cited by 3
“Perhaps the three most referred datasets in QA system research to date are SQuAD/SQuAD 2.0 (The Stanford Question Answering Dataset) [14] [15], CoQA (A Conversational Question Answering Challenge) [18] and QuAC (Question Answering in Context) [8].”
BackgroundInfluentialZhang, X., Huang, H., Chi, Z., & Mao, X.-L. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.857 · Cited by 4
AIA-BDE: A Corpus of FAQs in Portuguese and their Variations
Oliveira, H. G., Ferreira, J., Santos, J., Fialho, P., Rodrigues, R., Coheur, L., & Alves, A. (2020) · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · Cited by 3
Liu, Z., Lim, H., Suhaimi, N. F. A., Tong, S. C., Ong, S., Ng, A., Lee, S., MacDonald, M. R., Ramasamy, S., Krishnaswamy, P., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.03530 · Cited by 3
“CoQA (Reddy et al., 2018) and QuAC (Choi et al., 2018) are designed to mimic multi-turn information-seeking discussions of the given material.”
BackgroundQuestion Answering for Privacy Policies: Combining Computational and Legal PerspectivesPreprint
Ravichander, A., Black, A. W., Wilson, S., Norton, T. B., & Sadeh, N. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.00841 · Cited by 51
“Our evaluation metric for answer-sentence selection is sentence-level F1, implemented similar to (Choi et al., 2018; Rajpurkar et al., 2016).”
Using Dynamic Embeddings to Improve Static Embeddings.Preprint
Wang, Y., Cui, L., & Zhang, Y. (2019) · arXiv (Cornell University) · Cited by 2
Explicit Memory Tracker with Coarse-to-Fine Reasoning for Conversational Machine ReadingPreprint
Gao, Y., Wu, C.-S., Joty, S., Xiong, C., Socher, R., King, I., Lyu, M. R.-T., & Hoi, S. C. H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.12484 · Cited by 3
“ShARC Conversational Machine Reading (Saeidi et al., 2018) differs from conversational question answering (Choi et al., 2018; Reddy et al., 2019) and conversational question generation (Gao et al., 2019) in that 1) machines are required to formulate follow-up questions to fill the information gap,…”
BackgroundQuerent Intent in Multi-Sentence QuestionsPreprint
Burchell, L., Chi, J., Hosking, T., Markl, N., & Webber, B. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.08980 · Cited by 3
“Prior work on QA has focused on either single questions contained within dialogue (Choi et al., 2018; Reddy et al., 2019; Saeidi et al., 2018; Clark et al., 2018), or questions composed of two or more sentences crowd-sourced by community QA (cQA) services (John and Kurian, 2011; Tamura et al.,…”
DialogueBERT: A Self-Supervised Learning based Dialogue Pre-training EncoderPreprint
Zhang, Z., Guo, T., & Chen, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.10480 · Cited by 3
“, some researchers transformed the dialoguemodeling problem into aMachine Reading Comprehension (MRC) problem [1, 2, 12].”
BackgroundSegmenting Brazilian legislative text using weak supervision and active learning
Siqueira, F., Pressato, D., Pereira, F. S. F., da Silva, N. F. F., de Souza, E. P. R., Dias, M. D. S., & de Carvalho, A. C. P. L. F. (2024) · Artificial Intelligence and Law · doi:10.1007/s10506-024-09419-5 · Cited by 2
GASCOM: Graph-based Attentive Semantic Context Modeling for Online Conversation Understanding
Agarwal, V., Chen, Y., & Sastry, N. (2024) · Online Social Networks and Media · doi:10.1016/j.osnem.2024.100290 · Cited by 2
Temporal Extraction and Retrieval
Alonso, O., & Spaniol, M. (2024) · ACM eBooks · doi:10.1145/3674127.3674137 · Cited by 2
Tonellotto, N. (2024) · ACM eBooks · doi:10.1145/3674127.3674130 · Cited by 3
CoSRec: A Joint Conversational Search and Recommendation Dataset
Alessio, M., Merlo, S., Noia, T. D., Faggioli, G., Ferrante, M., Ferro, N., Muntean, C. I., Nardini, F. M., Narducci, F., Perego, R., et al. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3730319 · Cited by 3
Similarity or deeper understanding? Analyzing the TED-Q dataset of evoked questions
Westera, M., Amidei, J., & Mayol, L. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.439 · Cited by 2
“For example, in terms of available datasets, the QuAC (’Question Answering in Context’) dataset (Choi et al., 2018) consists of 100K questions asked in unscripted dialogue about a given topic; Rao and Daumé III (2018) present a dataset of”
MethodABSA-Bench: Towards the Unified Evaluation of Aspect-based Sentiment Analysis Research
Das, A., & Zhang, W. E. (2020) · Cited by 2
Rach, N., Schindler, C., Feustel, I., Daxenberger, J., Minker, W., & Ultes, S. (2021) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/2021.sigdial-1.39 · Cited by 2
“This limitation renders the corresponding systems inflexible, especially in comparison to recent data-driven approaches in domains like question answering (Choi et al., 2018).”
ResultDuReSE: Rewriting Incomplete Utterances via Neural Sequence Editing
Jiang, W., Gu, X., Chen, Y., & Shen, B. (2023) · Neural Processing Letters · doi:10.1007/s11063-023-11174-8 · Cited by 2
“Each training sample consists of an original query from the QuAC dataset [4], its context (dialogue history), and the corresponding reformulation that is manually rewritten by humans.”
BackgroundBullshit, Pragmatic Deception, and Natural Language Processing
Deck, O. (2023) · Dialogue & Discourse · doi:10.5210/dad.2023.103 · Cited by 2
“…in stages of the bullshit detection process: claim identification (e.g. Hassan et al., 2015; Barrón-Cedeño et al., 2020), question answering (e.g. Choi et al., 2018; Kim et al., 2021), question/answer congruity (e.g. Faruqui and Das, 2018; Yu and Jiang, 2021), argument quality (e.g.…”
PragmatiCQA: A Dataset for Pragmatic Question Answering in Conversations
Qi, P., Du, N., Manning, C. D., & Huang, J. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.385 · Cited by 4
NegT5: A Cross-Task Text-to-Text Framework for Negation in Question Answering
Jin, T., Racharak, T., & Nguyen, L.-M. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-5837-5_23 · Cited by 2
Huang, C., Qin, P., Lei, W., & Lv, J. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.670 · Cited by 3
“For example, Braun et al. (2017); Choi et al. (2018) require the human to answer the questions in a pre-collected question set and use these answers as ground truth to evaluate the model-generated results.”
BackgroundKongyoung, S., Macdonald, C., & Ounis, I. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.913 · Cited by 2
“For the question rewriting task, we use the test sets of the OR-QuAC dataset.”
MethodInfluentialHu, J., Wu, L., Chen, Y., Hu, P., & Zaki, M. J. (2024) · Machine Intelligence Research · doi:10.1007/s11633-023-1421-0 · Cited by 3
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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