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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]
Experimental — accuracy not yet measuredIn 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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Citations
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
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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Obionwu, C. V., Mukherjee, D., Devadas, V., Mittal, S., Ghumman, A. N., John, A. K., Buch, A., Nuernberger, A., & Saake, G. (2024) · Lecture notes in educational technology · doi:10.1007/978-981-97-3883-0_3 · Cited by 2
Verberne, S. (2024) · ACM eBooks · doi:10.1145/3674127.3674141 · Cited by 5
Ferro, N., & Maistro, M. (2024) · ACM eBooks · doi:10.1145/3674127.3674132 · Cited by 3
Privacy in Information Retrieval
Soboroff, I. M. (2024) · ACM eBooks · doi:10.1145/3674127.3674139 · Cited by 2
Enhanced question understanding for multi-type legal question answering
Yin, Y., Li, L., Xie, S., Tao, X., & Zhang, J. (2024) · CCF Transactions on Pervasive Computing and Interaction · doi:10.1007/s42486-024-00175-8 · Cited by 3
“Moreover, the applicability of our approach to general question answering domains (Rajpurkar et al. 2016, 2018; Choi et al. 2018; Yang et al. 2018; Li et al. 2022; Tang and Tung 2023) necessitates further investigation.”
Query Understanding in LLM-based Conversational Information Seeking
Yuan, Y., Abbasiantaeb, Z., Deng, Y., & Aliannejadi, M. (2025) · International World Wide Web Conference (WWW) · doi:10.1145/3701716.3715869 · Cited by 2
“Typically, the system handles unanswerable queries passively by responding with No Answer [16] if there is no direct information thatmatches the query.”
Cooking with context: Leveraging context for procedural question answering
Frummet, A., Elsweiler, D., & Kruschwitz, U. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2025.104212 · Cited by 2
What Did You Refer to? Evaluating Co-References in Dialogue
Zhang, W., Zhang, Y., Tang, H., Zhao, Z., Zhu, C., & Liu, T. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.450 · Cited by 2
“Conversational QA Recent research on conversational question answering ( ConvQA ) had been driven by two challenges, namely CoQA (Reddy et al., 2018) and QuAC (Choi et al., 2018).”
MethodMahmud-Uz-Zaman, M., Schaffer, S., & Scheffler, T. (2021) · Cited by 1
Jiang, Y., Hadifar, A., Deleu, J., Demeester, T., & Develder, C. (2022) · DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering (DialDoc) · doi:10.18653/v1/2022.dialdoc-1.12 · Cited by 2
“Most prior research on document-grounded dialog systems assumes a single document for each dialog (Choi et al., 2018; Reddy et al., 2019; Feng et al., 2020).”
BackgroundTAGNet: a tiny answer-guided network for conversational question generation
Wang, Z., Zhu, H., Liu, M. Z., & Qin, B. (2022) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-022-01737-x · Cited by 2
“We conduct experiments to evaluate our model on another dataset QuAC [7].”
BackgroundMethodInfluentialTran, N., & Litman, D. J. (2022) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/2022.sigdial-1.36 · Cited by 3
“Our work is related to recent work in knowledge identification (KI) in knowledge-grounded dialogues (Choi et al., 2018; Dinan et al., 2019; Qu et al., 2020; Feng et al., 2020; Campos et al., 2020; Wu et al., 2021).”
BackgroundOrca: A Few-shot Benchmark for Chinese Conversational Machine Reading Comprehension
Chen, N., Li, H., He, J., Bao, Y., Lin, X., Yang, Q., Liu, J., Gan, R., Zhang, J., Wang, B., et al. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.1050 · Cited by 2
Machine learning based review on Development and Classification of Question-Answering Systems
Uttarwar, S., Gambani, S., Thakkar, T., & Mulla, N. (2019) · International Conference on Computing Methodologies and Communication (ICCMC) · doi:10.1109/iccmc.2019.8819667 · Cited by 2
CAT-BERT: A Context-Aware Transferable BERT Model for Multi-turn Machine Reading Comprehension
Chen, C., Huang, X., Ji, F., Wang, C., Qiu, M., Huang, J., & Zhang, Y. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-73197-7_10 · Cited by 2
“, two multi-turn MRC tasks (QuAC [3] and CoQA [19]), and one single-turn MRC task (SQuAD 2.”
BackgroundMethodZhang, T., Wang, C., Qiu, M., Yang, B., Cai, Z., He, X., & Huang, J. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.197 · Cited by 2
A Practical 2-step Approach to Assist Enterprise Question-Answering Live Chat
Liao, L.-Y., & Fares, T. (2021) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/2021.sigdial-1.48 · Cited by 2
“, 2020), then a MRC model, known as an extractive reader, to do span extraction from those passages and mark where the answers are (Rajpurkar et al., 2016; Choi et al., 2018).”
BackgroundUnsupervised Multiple Choices Question Answering: Start Learning from Basic Knowledge
Liu, C.-L., & Lee, H.-Y. (2021) · Workshop on Machine Reading for Question Answering (MRQA) · doi:10.18653/v1/2021.mrqa-1.12 · Cited by 4
“Recently, numerous question answering datasets (Weston et al., 2015; Rajpurkar et al., 2016, 2018; Yang et al., 2018; Trischler et al., 2017; Choi et al., 2018; Joshi et al., 2017; Kwiatkowski et al., 2019; Reddy et al., 2019; Richardson, 2013; Lai et al., 2017a; Khashabi et al., 2018) have been…”
BackgroundKaddari, Z., Berrich, J., Rahmoun, N., Belouali, S., & Bouchentouf, T. (2021) · International Conference on Intelligent Computing in Data Sciences (ICDS) · doi:10.1109/icds53782.2021.9626759 · Cited by 2
A Context-Aware Model with Flow Mechanism for Conversational Question Answering
Song, D., Yang, Y. T., & Ailian, F. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-92310-5_72 · Cited by 2
Varghese, N., & Punithavalli, M. (2022) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-16-9113-3_1 · Cited by 2
Hierarchical conversation flow transition and reasoning for conversational machine comprehension
Liu, X., Yang, M., Lyu, Z., Lin, D., Li, P., & Xu, R. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07720-5 · Cited by 2
“Similar to [3], for QuAC, we also use the human equivalence score (HEQ) to measure the percentage of examples where the F1 score exceeds or matches human F1.”
BackgroundMethodInfluentialDo I have the Knowledge to Answer? Investigating Answerability of Knowledge Base Questions
Patidar, M., Faldu, P., Singh, A., Vig, L., Bhattacharya, I., & Mausam, M. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.576 · Cited by 3
NewsDialogues: Towards Proactive News Grounded Conversation
Li, S., Yin, Y., Yang, C., Jiang, W., Li, Y., Cheng, Z., Shang, L., Jiang, X., Liu, Q., & Yang, Y. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.224 · Cited by 4
“For example, in QuAC (Choi et al., 2018), doc2dial (Feng et al.”
BackgroundMethodInfluentialTowards End-to-End Open Conversational Machine Reading
Zhou, S., Ouyang, S., Zhang, Z., & Zhao, H. Y. (2023) · Findings of the Association for Computational Linguistics: EACL · doi:10.18653/v1/2023.findings-eacl.154 · Cited by 3
“The mainstream of research on the conversation-based reading comprehension task focuses on either the decision making (Choi et al., 2018; Reddy et al., 2019; Sun et al., 2019; Cui et al., 2020) or the follow-up utterance generation (Bi et al., 2019; Gao et al., 2020a).”
BackgroundPREME: Preference-based Meeting Exploration through an Interactive Questionnaire
Arabzadeh, N., Ahmadvand, A., Kiseleva, J., Liu, Y., Awadallah, A. H., Zhong, M., & Shokouhi, M. (2023) · Findings of the Association for Computational Linguistics: EACL · doi:10.18653/v1/2023.findings-eacl.25 · Cited by 5
The CLEF 2024 Monster Track: One Lab to Rule Them All
Ferro, N., Gonzalo, J. A., Jussi Karlgren, C., & Müller, H. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-56072-9_2 · Cited by 2
A guide to the dataset explosion in QA, NLI, and commonsense reasoning
Rogers, A., & Rumshisky, A. (2020) · doi:10.18653/v1/2020.coling-tutorials.5 · Cited by 2
“Questions are more difficult if they are collected independently from the text (Kwiatkowski et al., 2019), written from summaries (Trischler et al., 2016) or hints (Choi et al., 2018).”
BackgroundA Question-Answering System that Can Count
Lokman, A. S., Ameedeen, M. A., & Ghani, N. A. (2021) · Lecture notes in electrical engineering · doi:10.1007/978-981-33-4069-5_6 · Cited by 2
Interactive Machine Comprehension with Dynamic Knowledge Graphs
Yuan, X. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.540 · Cited by 3
“…(Hill et al., 2015; Chen et al., 2016; Rajpurkar et al., 2016; Trischler et al., 2016a; Nguyen et al., 2016; Reddy et al., 2018; Yang et al., 2018a; Choi et al., 2018; Clark et al., 2020) and novel models (Trischler et al., 2016b; Wang and Jiang, 2016; Seo et al., 2016; Wang et al., 2017; Huang et…”
A Simple Approach to Jointly Rank Passages and Select Relevant Sentences in the OBQA Context
Luo, M., Chen, S., & Baral, C. R. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop (NAACL SRW) · doi:10.18653/v1/2022.naacl-srw.23 · Cited by 2
On the Robustness of Question Rewriting Systems to Questions of Varying Hardness
Ye, H., Ng, H. T., & Han, W. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.149 · Cited by 2
“3 Question-in-Context Rewriting Question-in-context rewriting (QR) aims to generate a self-contained rewrite from a context-dependent question in CQA.”
BackgroundMethodInfluentialCareful Selection of Knowledge to solve Open Book Question AnsweringPreprint
Banerjee, P., Pal, K. K., Mitra, A., & Baral, C. R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.10738 · Cited by 14
“…answering (Rajpurkar et al., 2016; Joshi et al., 2017; Khashabi et al., 2018; Richardson et al., 2013; Lai et al., 2017; Reddy et al., 2018; Choi et al., 2018; Tafjord et al., 2018; Mitra et al., 2019) and many attempts have been made to solve these challenges (Devlin et al., 2018; Vaswani…”
R3: A Reading Comprehension Benchmark Requiring Reasoning ProcessesPreprint
Wang, R., Tao, K., Song, D., Zhang, Z., Ma, X., Su, X. H., & Dai, X. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.01251 · Cited by 4
“CoQA (Reddy et al., 2019) and QuAC (Choi et al., 2018) are introduced to evaluate how reading comprehension models aggregate information and answer questions in the context of a conversation.”
BackgroundPrerequisites for Explainable Machine Reading Comprehension: A Position Paper.Preprint
Sugawara, S., Stenetorp, P., & Aizawa, A. (2020) · arXiv (Cornell University) · Cited by 2
Composing Answer from Multi-spans for Reading ComprehensionPreprint
Zhang, Z., Zhang, Y., Zhao, H., Zhou, X., & Zhou, X. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.06141 · Cited by 2
“Non-extraction style MRC tasks like Dureader (He et al. 2018) and MS MARCO (Nguyen et al. 2016) are required to generate an answer according to the given context and question.”
BackgroundMethodQuestion Generation for Supporting Informational Query Intents.Preprint
Yin, X., May, J., Li, Z., & Small, K. (2020) · arXiv (Cornell University) · Cited by 2
Role of Attentive History Selection in Conversational Information SeekingPreprint
Gupta, S., & Sharma, N. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.03749 · Cited by 2
Advances in Multi-turn Dialogue Comprehension: A SurveyPreprint
Zhang, Z., & Zhao, H. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.04984 · Cited by 24
Robustly Optimized and Distilled Training for Natural Language UnderstandingPreprint
ElFadeel, H., & Peshterliev, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.08809 · Cited by 1
BERT-CoQAC: BERT-based Conversational Question Answering in ContextPreprint
Zaib, M., Tran, D. H., Sagar, S., Mahmood, A., Zhang, W. E., & Sheng, Q. Z. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.11394 · Cited by 4
“Our model outperforms the baseline methods and BERT-HAE model on all the three metrics i.e. F1, HEQ-Q, and HEQ-D and answer our research questions as follows.”
BackgroundMethodInfluentialMulti-Turn Dialogue Comprehension from a Topic-Aware PerspectivePreprint
Ma, X., Xu, Y., Zhao, H., & Zhang, Z. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4585917 · Cited by 2
Learn a prior question-aware feature for machine reading comprehension
Zhang, Y., Shen, B., & Cao, X. (2022) · Frontiers in Physics · doi:10.3389/fphy.2022.1085102 · Cited by 1
“QApairs dataset relies on three source datasets, namely SQuAD [4], QuAC [29], CoQA [6].”
MethodExplicit History Selection for Conversational Question Answering
Zhang, Z., Feng, Q., & Wang, Y. (2022) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai56018.2022.00212 · Cited by 1
“We compare our model with various state-of-the-art base-lines: BiDAF++ [3], BiDAF++ w/2-ctx [3], BERT + HAE [7], BERT + PosHAE [14], GraphFlow [4], HAM [14], ExCorD [8].”
BackgroundMethodInfluentialDocTabQA: Answering Questions from Long Documents Using Tables
Wang, H., Hu, K., Dong, H., & Gao, L. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-70533-5_27 · Cited by 2
eCommerce Search and Discovery
Kallumadi, S. T., & King, T. H. (2024) · ACM eBooks · doi:10.1145/3674127.3674140 · Cited by 1
Continual debiasing: A bias mitigation framework for natural language understanding systems
Lee, M., Kim, J., Park, J.-H., & Lee, S. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.126593 · Cited by 1
Spike-Driven Lightweight Large Language Model With Evolutionary Computation
Zhang, M., Wei, W., Zhou, Z., Liu, W., Zhang, J., Belatreche, A., & Yang, Y. (2025) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2025.3606613 · Cited by 1
“This transition has led to substantial performance improvements across diverse NLP tasks, including question answering [35], [36], information extraction [37], [38], etc.”
Discourse-Aware Language Representation
Zhang, Z., Ouyang, S., & Zhao, H. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3621229 · Cited by 2
“…answer styles, conversational QA tasks can be roughly divided into generative [48] (to generate answer texts to given questions), extractive [44], [45] (to extract spans from given contexts to answer questions) and multi-choice [5] (to select the most appropriate answer among given answer…”
Explainable Semantic Text Relations: A Question-Answering Framework for Comparing Document Content
Aperstein, Y., Gottlib, A., Benita, G., & Apartsin, A. (2025) · Information · doi:10.3390/info16121090 · Cited by 1
“These tasks overlap; for example, paraphrases are bidirectional entailments and typically rank high in similarity, whereas contradictions receive low similarity scores.”
A survey of the state of the art in conversational question answering systems
Perera, M. M., Mahmood, A., Wijethilake, K. E., Islam, F., Tahermazandarani, M., & Sheng, Q. Z. (2025) · Knowledge and Information Systems · doi:10.1007/s10115-025-02620-1 · Cited by 3
“Question Answering in Context (QuAC) dataset [28] includes 100,000+ questions and answers in 14,000+ conversations.”
InfluentialWei, Y., Li, X., Huang, Z., Lin, Z., & Zhang, N. (2026) · Computers in Industry · doi:10.1016/j.compind.2026.104462 · Cited by 1
AILQA: Evaluating AI-driven legal question answering systems for the indian legal system
Nigam, S. K., Mishra, S. K., Shallum, N., Ghosh, K., & Bhattacharya, A. (2026) · Artificial Intelligence and Law · doi:10.1007/s10506-026-09537-2 · Cited by 1
“Question Answering (QA) is an artificial intelligence (AI) task that utilizes Natural Language Processing (NLP) to interpret and respond to queries in natural language, mimicking human interaction (Allam and Haggag, 2012; Choi et al., 2018).”
Graph-based Dynamic Word Embeddings
Lu, Y., Cheng, X., Liang, Z., & Rao, Y. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/594 · Cited by 2
Docalog: Multi-document Dialogue System using Transformer-based Span Retrieval
Alavian, S. H., Satvaty, A., Sabouri, S., Asgari, E., & Sameti, H. (2022) · DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering (DialDoc) · doi:10.18653/v1/2022.dialdoc-1.16 · Cited by 1
“…restaurant or planning an event by understanding the conversation (Yan et al., 2017); and (iii) QA dialog systems attempting to answer the user exploiting information deducted from a collection of seen documents or a knowledge base, for instance CoQA (Reddy et al., 2019), QuAC (Choi et al., 2018).”
BackgroundMethodInfluentialXu, S., Wan, X., Hu, S., Zhou, M., Xu, T., Wang, H., & Mi, H. (2022) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3534678.3539116 · Cited by 1
“In some tasks, the questions are also utterances in the conversation [5, 18].”
BackgroundSupervised Contrastive Learned Deep Model for Question Continuation Evaluation
Sun, B., Li, H., He, J. Y. D., & Zhang, Y. (2023) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2023.3271625 · Cited by 1
“As only a few models use the QCE task as a mission and no model is specifically aimed at this task, we compare our TLMR model with the baseline provided in [1].”
BackgroundMethodInfluentialDeep QA: An Open-Domain Dataset of Deep Questions and Comprehensive Answers
Anbarasu, H. S., Navalli, H. V., Vidapanakal, H., Gowd, K. M., & Das, B. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-35299-7_16 · Cited by 1
Robust Domain Adaptation for Machine Reading Comprehension
Jiang, L., Huang, Z. H., Liu, J., Wen, Z., & Peng, X. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i7.25974 · Cited by 2
Improved Instruction Ordering in Recipe-Grounded Conversation
Le, D., Guo, R., Xu, W. H., & Ritter, A. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.561 · Cited by 7
SSP: Self-Supervised Post-training for Conversational Search
Tu, Q., Gao, S., Wu, X., Cao, Z., Wen, J.-R., & Yan, R. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.837 · Cited by 1
Developing organizational context adjustments in project-based organizations (part 1)
Алондериене, Р., & Бусеро, А. (2023) · Управление проектами и программами · doi:10.36627/2075-1214-2023-3-3-166-175 · Cited by 1
Contextualizing and Expanding Conversational Queries without Supervision
Krasakis, A. M., Yates, A., & Kanoulas, E. (2023) · ACM Transactions on Information Systems · doi:10.1145/3632622 · Cited by 1
“Large-scale datasets have greatly improved the ability of models to understand language across a variety of tasks [11, 53, 73], but such datasets are lacking when it comes to conversational retrieval.”
BackgroundLiu, Z., Chen, Z., Ren, Z., Gao, S., Ma, J., & Ren, P. (2023) · Information Processing & Management · doi:10.1016/j.ipm.2023.103578 · Cited by 1
Multi-User MultiWOZ: Task-Oriented Dialogues among Multiple Users
Jo, Y., Zhao, X., Biswas, A., Bassiou, N., Auvray, V., Malandrakis, N., Metallinou, A., & Potamianos, A. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.213 · Cited by 1
Contrastive Learning for Inference in Dialogue
Ishii, E., Xu, Y., Wilie, B., Ji, Z., Lovenia, H., Chung, W., & Fung, P. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.631 · Cited by 1
Interpreting Indirect Answers to Yes-No Questions in Multiple Languages
Wang, Z., Hossain, M. D., Mathur, S., Melo, T. C. S., Özler, K. B., Park, K., Quintero, J., Rezaei, M., Shakya, S. N., Uddin, M., et al. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.146 · Cited by 1
QookA: A Cooking Question Answering Dataset
Frummet, A., & Elsweiler, D. (2024) · Conference on Human Information Interaction and Retrieval · doi:10.1145/3627508.3638311 · Cited by 2
“As mentioned in the introduction, datasets like QuAC [2], CoQA [10], and others can be used for fine-tuning question answering models.”
MethodNORMY: Non-Uniform History Modeling for Open Retrieval Conversational Question Answering
Rashid, M. S., Meem, J. A., & Hristidis, V. (2024) · IEEE International Conference on Semantic Computing (ICSC) · doi:10.1109/icsc59802.2024.00022 · Cited by 5
“The multi turn questions can be interconnected (CoQA [1], DoQA [42]), can depend on the previous history answer(QuAC [2]) or only limited to binary answers (ShARC [20]).”
BackgroundInfluentialLightLT: A Lightweight Representation Quantization Framework for Long-Tail Data
Wang, H., Li, R., Wang, Z., Tang, X., Zhang, D., Cheng, M., Yin, B., Droppo, J., Wang, S., & Gao, J. (2024) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde60146.2024.00114 · Cited by 1
An Analysis of Stopping Strategies in Conversational Search Systems
Fu, X., Pérez‐Ortiz, M., & Lipani, A. (2024) · ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR) · doi:10.1145/3664190.3672524 · Cited by 2
“Additionally, many studies also focus on the usage of information during the retrieval [2, 11].”
Ricketts, J., Guo, W., Pelham, J., & Barry, D. J. (2024) · Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability · doi:10.1177/1748006x241272831 · Cited by 1
Galuščáková, P., Oard, D. W., & Nair, S. (2024) · ACM eBooks · doi:10.1145/3674127.3674136 · Cited by 2
Gómez, E., Cuesta, H., Gkiokas, A., Gómez-Cañón, J. S., Porcaro, L., & Yesiler, F. (2024) · ACM eBooks · doi:10.1145/3674127.3702959 · Cited by 1
Baeza-Yates, R. A., Murgai, L., & Dudy, S. (2024) · ACM eBooks · doi:10.1145/3674127.3674138 · Cited by 1
Spoken Conversational Search for General Knowledge
Rojas-Barahona, L. M., Bellec, P., Besset, B., Dos-Santos, M., Heinecke, J., Asadullah, M., Le-Blouch, O., Lancien, J., Damnati, G., Mory, E., et al. (2019) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/w19-5914 · Cited by 3
“Not long ago, each of these research subjects were studied separately; only very recently has studying the intersection between them gained increasing interest (Reddy et al., 2018; Choi et al., 2018).”
BackgroundAnswer-Supervised Question Reformulation for Enhancing Conversational Machine Comprehension
Li, Q., Su, H., Niu, C., Wang, D., Li, Z., Feng, S., & Zhang, Y. (2019) · Workshop on Machine Reading for Question Answering (MRQA) · doi:10.18653/v1/d19-5805 · Cited by 2
“We validate the effectiveness of our ASQR model on QuAC dataset (Choi et al., 2018).”
BackgroundMethodInfluentialWhen in Doubt, Ask: Generating Answerable and Unanswerable Questions, Unsupervised
Nikolenko, L., & Kalehbasti, P. R. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-84060-0_2 · Cited by 1
“an 20 points. The results of their work shows a huge potential for further research on conversational question answering which is key for natural human-machine communication. Previously, Choi et al. (Choi et al., 2018) had conducted a similar study on conversational question answering, and using high-performance langu…”
Transfer Learning on Natural YES/NO Questions
Yin, H., Zhou, F., Li, X., Junqiang, Z., & Liu, K. (2020) · IEEE International Conference on Software Engineering and Service Science (ICSESS) · doi:10.1109/icsess49938.2020.9237654 · Cited by 1
“YES/NO QA appears as a subset of some existing datasets such as CoQA [7], QuAC [8] and Hot-PotQA [1], while those datasets are mainly for other aspect of QA, such as conversational QA or multi-step reasoning, not the naturally occurring questions.”
MethodVakulenko, S., Kanoulas, E., & de Rijke, M. (2021) · ACM Transactions on Information Systems · doi:10.1145/3466796 · Cited by 8
“For example, the QuAC dataset [10] consists of dialogues that contain sequences of question-answer pairs.”
A Dialogue Contextual Flow Model for Utterance Intent Recognition in Multi-turn Online Conversation
Zhang, Z., Guo, T., Jiang, L., & Gu, M. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-82147-0_21 · Cited by 1
Kannan, M., Ponnusamy, H. S., Kuthy, K. D., Stein, L., & Meurers, D. (2021) · International Conference on Natural Language Generation · doi:10.18653/v1/2021.inlg-1.3 · Cited by 1
“These datasets, such as SQuAD (Ra-jpurkar et al., 2016), Quac (Choi et al., 2018), and Coqa (Reddy et al., 2019), are not well-suited for training models for tasks requiring high question-answer congruence, and they focus on English.”
BackgroundRelying on Discourse Analysis to Answer Complex Questions by Neural Machine Reading Comprehension
Galitsky, B. A., Ilvovsky, D., & Goncharova, E. (2021) · International conference Recent advances in natural language processing · doi:10.26615/978-954-452-072-4_051 · Cited by 1
“As the example of complex questions datasets, we consider NewsQA (Trischler et al., 2017), QA in Context (QuAC) dataset (Choi et al., 2020), and multi-sentence questions (MSQ) (Burchell et al., 2020).”
MethodAIA-BDE: um Corpo de Perguntas, Variações e outras Anotações
Oliveira, H. G., & Alves, A. (2021) · Linguamática · doi:10.21814/lm.13.2.350 · Cited by 1
Incorporating Phrases in Latent Query Reformulation for Multi-Hop Question Answering
Tang, J., Hu, S., Chen, Z., Xu, H., & Tan, Z. (2022) · Mathematics · doi:10.3390/math10040646 · Cited by 3
Exploring the Utility of Dutch Question Answering Datasets for Human Resource Contact Centres
van Toledo, C., Schraagen, M., van Dijk, F., Brinkhuis, M. J. S., & Spruit, M. R. (2022) · Information · doi:10.3390/info13110513 · Cited by 3
Conversational Machine Comprehension
Gao, J., Xiong, C., Bennett, P. N., & Craswell, N. (2023) · The information retrieval series · doi:10.1007/978-3-031-23080-6_5 · Cited by 1
Li, X., Zou, B., Dong, M., Yao, J., & Hong, Y. (2022) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai56018.2022.00053 · Cited by 1
“[11] demonstrate that the questioner starts dialog by asking general questions about a topic of interest and progresses to more specific ones.”
BackgroundInfluentialJeong, S., Baek, J., Hwang, S. J., & Park, J. (2023) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.35 · Cited by 2
Interpreting Answers to Yes-No Questions in User-Generated Content
Mathur, S., Park, K., Chinnappa, D., Kotamraju, S., & Blanco, E. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.942 · Cited by 1
CROWN: Conversational Passage Ranking by Reasoning over Word NetworksPreprint
Kaiser, M., Roy, R. S., & Weikum, G. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.02850 · Cited by 1
“There are several benchmarks available regarding conversational reading comprehension, like QBLink [8], CoQA [17], QuAC [5] and ShARC [19].”
Zhang, T., Wang, C., Qiu, M., Yang, B., He, X., & Huang, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.10327 · Cited by 1
“Since people are concerned about the scientific explanation and sources of answers in the medical domain, we ask annotators to select the support sentence of their annotated answer similar to those of CoQA (Reddy, Chen, and Manning 2019) and QuAC (Choi et al. 2018).”
Tracing Origins: Coref-aware Machine Reading Comprehension.Preprint
Huang, B., Zhang, Z., & Zhao, H. (2021) · arXiv (Cornell University) · Cited by 1
Structural Modeling for Dialogue Disentanglement.Preprint
Ma, X., Zhang, Z., & Zhao, H. (2021) · arXiv (Cornell University) · Cited by 1
On the data scarcity problem of neural-based named entity recognition
Zhou, R. (2023) · doi:10.32657/10356/173481 · Cited by 1
Infinite-parameter Large Language ModelPreprint
Ding, F. (2024) · doi:10.33774/coe-2024-7khtw
What Do You Mean ‘Why?’: Resolving Sluices in Conversations
Hansen, V. P. B., & Søgaard, A. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6295
Answering Conversational Questions on Structured Data without Logical FormsPreprint
Müller, T., Piccinno, F., Nicosia, M., Shaw, P. M., & Altün, Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.11787
“In recent years, there has been significant progress on conversational question answering (QA), where questions can be meaningfully answered only within the context of a conversation (Iyyer et al., 2017; Choi et al., 2018; Saha et al., 2018).”
BackgroundMaquera, F. I. M., Castro-Gutiérrez, E., & Valderrama, A. P. (2019) · LACCEI International Multi-Conference for Engineering, Education and Technology (LACCEI) · doi:10.18687/laccei2019.1.1.178
“Among all existing datasets, the most popular ones are: SQuAD [13], MS Marco [14], bAbI [15], QuAC [16], CoQA [17] and WikiQA [18].”
Method
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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