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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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“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
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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
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Items 601–700 of 704
Spoken Conversational Search for General KnowledgePreprint
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) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.11980
“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).”
BackgroundLi, J., Liu, M., Qin, B., Zheng, Z., & Liu, T. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.03514 · Cited by 1
“I will briefly introduce two datasets QuAC (Choi et al., 2018)and CoQA (Reddy et al.”
InfluentialRecurrent Chunking Mechanisms for Conversational Machine Reading Comprehension
Gong, H., Shen, Y., Yu, D., Chen, J., & Yu, D. (2019)
Yang, Z., Zhu, C., Sachidananda, V., & Darve, E. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.03753 · Cited by 2
“Grounded language information has been observed to augment model performance on a wide variety of natural language processing and understanding tasks (He et al., 2017; Choi et al., 2018).”
BackgroundLiu, C.-L., & Lee, H.-Y. (2020) · arXiv (Cornell University)
Combining Impression Feature Representation for Multi-turn Conversational Question Answering
Jing, S., Hong, S., Zhao, D., Xie, H., & Tang, Z. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-63031-7_5
“BiDAF++ w/2-ctx (Choi et al., 2018): Based on BiDAF++, BiDAF++ w/ r -ctx consider the context(ctx) the previous r QA pairs.”
BackgroundMethodInfluentialBERT Embeddings Can Track Context in Conversational SearchPreprint
Ferreira, R., Semedo, D., & Magalhães, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.06529
“Anexample of this is showed in [5], where researchers created the CANARD dataset by manually rewriting the queries in QuAC [6] (a conversational QA dataset about a single passage) to train a model in the conversational query rewriting task.”
MethodSemantic Representation for Dialogue ModelingPreprint
Bai, X., Chen, Y., Song, L., & Zhang, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.10188
“There are two salient subtasks in dialogue modeling, namely dialogue understanding (Choi et al., 2018; Reddy et al., 2019; Yu et al., 2020) and response generation (Li et al.”
BackgroundCAiRE in DialDoc21: Data Augmentation for Information-Seeking Dialogue SystemPreprint
Ishii, E., Xu, Y., Winata, G. I., Lin, Z., Madotto, A., Liu, Z., Xu, P., & Fung, P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.03530
“, 2019), QuAC (Choi et al., 2018), and DoQA (Campos et al.”
NOVA at TREC 2020 Conversational Assistance Track.
Ferreira, R., Semedo, D., & Magalhães, J. (2020) · Text REtrieval Conference
Prediction or Comparison: Toward Interpretable Qualitative Reasoning
Ren, M., Huang, H., & Gao, Y. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.59
Kim, G., Kim, H., Park, J., & Kang, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.11575
“Following Choi et al. (2018), we use the F1, HEQ-Q, and HEQ-D for QuAC and CANARD.”
InfluentialEviDR: Evidence-Emphasized Discrete Reasoning for Reasoning Machine Reading ComprehensionPreprint
Zhou, Y., Bao, J., Sun, H., Liang, J., Wu, Y., He, X., Zhou, B., & Zhao, T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.07994
Iconary: A Pictionary-Based Game for Testing Multimodal Communication with Drawings and TextPreprint
Clark, C., Salvador, J., Schwenk, D., Bonafilia, D., Yatskar, M., Kolve, E., Herrasti, A., Choi, J., Mehta, S., Skjonsberg, S., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2112.00800
“A similar format can be found in dialogue QA datasets (Reddy et al., 2019; Choi et al., 2018; Aliannejadi et al., 2019), and task-oriented dialogue in general similarly requires understanding the intent of a human communicator (Young et al., 2013; Chen et al., 2017).”
BackgroundSelf-citationTWEETSUMM -- A Dialog Summarization Dataset for Customer ServicePreprint
Feigenblat, G., Gunasekara, C., Sznajder, B., Joshi, S., Konopnicki, D., & Aharonov, R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.11894 · Cited by 8
“…datasets support dialog tasks such as next response prediction (Kadlec et al., 2015; Bor-des et al., 2016; Byrne et al., 2019), conversational question answering (Reddy et al., 2019; Choi et al., 2018; Saeidi et al., 2018) and dialog state tracking (Budzianowski et al., 2018; Rastogi et al., 2019).”
BackgroundDiscourse means for maintaining a proper rhetorical flow
Galitsky, B. A. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-824521-7.00005-3
Sequential multi‐headed attention for entity‐based relational neural networks
Moges, D., Zhao, J., & Qu, H. (2022) · Expert Systems · doi:10.1111/exsy.12945
“Machine reading comprehension (MRC) also known as Question and Answering (QA), is a fundamental goal of natural language understanding (NLU) with a wide range of applications (Ben Abacha & Zweigenbaum, 2012; Choi et al., 2018; Reddy et al., 2019; Zhang, Yang, & Zhao, 2020; Zhang, Zhao, et al., 2020).”
BackgroundMulti-stage Knowledge Enhancement for Ultra-fine-grained Entity Typing
Yang, Y., & Li, N. (2022) · International Symposium on Autonomous Systems (ISAS) · doi:10.1109/isas55863.2022.9757340
“Extracting fine-grained types is beneficial for many downstream context-sensitive tasks, such as entity-linking [2][3], relation extraction [4][5], co-reference resolution [6][7], and question answering [8][9].”
BackgroundRelying on discourse analysis to answer complex questions by neural machine reading comprehension
Galitsky, B. A. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-824521-7.00012-0
Wang, Z., & Komatani, K. (2022) · DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering (DialDoc) · doi:10.18653/v1/2022.dialdoc-1.8
“CA-NARD (Elgohary et al., 2019) rewrites QuAC’s questions and introduces this rewriting to the QR task.”
BackgroundA Semi-automatic Data Generator for Query Answering
Angiulli, F., Prete, A. D., Fassetti, F., & Nisticò, S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16564-1_11
Exploring neural question generation for formal pragmatics: Data set and model evaluation
Kuthy, K. D., Kannan, M., Ponnusamy, H. S., & Meurers, D. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.966013
“…(Rajpurkar et al., 2016), the Conversational Question Answering dataset (CoQA) ( Reddy et al., 2019), and the Question Answering in Contex dataset (QuAC) (Choi et al., 2018) have been created for this task, providing sets of questions and the text passages that contain the requested information.”
BackgroundMachine Reading Comprehension for Continuous Questions via Multi-Task Learning
Liu, S., Yang, K., Ding, K., Liu, L., & Zhang, X. (2022) · International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE) · doi:10.1109/icbaie56435.2022.9985870
“Owing to the release of large-scale CMC datasets, e.g. CoQA [6] and QuAC [7], research on CMC has enjoyed a huge surge in recent years.”
BackgroundQuestion Answering Based on Entity-Aware Self-attention
Chen, J., Fan, H., Wu, H., Zhu, H., & Yang, C. (2022) · Communications in computer and information science · doi:10.1007/978-981-19-8915-5_6
A Commonsense-Enhanced Document-Grounded Conversational Agent: A Case Study on Task-Based Dialogue
Strathearn, C., & Gkatzia, D. (2022) · Signals and communication technology · doi:10.1007/978-3-031-11035-1_6 · Cited by 1
Evaluating Conversational Information Retrieval
Gao, J., Xiong, C., Bennett, P. N., & Craswell, N. (2023) · The information retrieval series · doi:10.1007/978-3-031-23080-6_2
Gao, J., Xiong, C., Bennett, P. N., & Craswell, N. (2023) · The information retrieval series · doi:10.1007/978-3-031-23080-6_3
Sentence Retrieval for Open-Ended Dialogue Using Dual Contextual Modeling
Harel, I., Taitelbaum, H., Szpektor, I., & Kurland, O. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-28244-7_27
Adithya, M. S., Ahmed, M., Kestur, M. M., Chaithanya, A. S., & Das, B. (2023) · International Conference on Computer and Automation Engineering · doi:10.1109/iccae56788.2023.10111327
“There are many datasets that cater to these tasks, including HotpotQA [1], SQuAD [2], and QuAC [3] for NLQA.”
MethodResolving Indirect Referring Expressions for Entity Selection
Hosseini, M. J., Radlinski, F., Pareti, S., & Louis, A. P. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.688
ConvRGX: Recognition, Generation, and Extraction for Self-trained Conversational Question Answering
Zhang, T., Tang, L., Fang, W., Luo, H., Wu, X., Meng, H. M. L., & Glass, J. R. (2023) · DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering (DialDoc) · doi:10.18653/v1/2023.dialdoc-1.10
“Recent studies have collected such corpora, but with human annotations on less than 1,000 documents (Choi et al., 2018; Feng et al., 2020).”
BackgroundMethodInfluentialMachine Reading Comprehension for Multiclass Questions on Thai Corpus
Lapchaicharoenkit, T. (2019) · Chula Digiverse · doi:10.58837/chula.the.2019.168
“This method of adding special token is similar to QuAC [3] where the authors of the dataset append special token [UNS] to the passage.”
BackgroundMethodInfluentialLi, X., Zou, B., Fan, Y., Li, Y., Aw, A. T., & Yu, H. Q. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.209 · Cited by 1
Strong and Efficient Baselines for Open Domain Conversational Question Answering
Coman, A. C., Barlacchi, G., & de Gispert, A. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.417
Few-shot Unified Question Answering: Tuning Models or Prompts?
Bansal, S., Yavuz, S., Pang, B., Bhat, M., & Zhou, Y. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.550 · Cited by 3
Multi-Granularity Information Interaction Framework for Incomplete Utterance Rewriting
Du, H., Zhang, D., Li, C., Li, Y., & Zhao, D. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.168
“Recently increasing attention has been paid to multi-turn dialogue modeling (Choi et al., 2018; Reddy et al., 2019; Sun et al., 2019) and the major challenge in this field is that speakers tend to use incomplete utterances for brevity, such as referring back to (i.e., co-reference) or omitting…”
BackgroundGenerating Extractive Answers: Gated Recurrent Memory Reader for Conversational Question Answering
Zhang, X., & Yang, Q. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.516
“…answering, such as BiDAF (Seo et al., 2017) and Rception (Zhang and Wang, 2020) in classic single-turn machine reading comprehension, BiDAF++ (Yatskar, 2018) in conversational machine reading comprehension, and other methods based on multi-modal or structured knowledge question answering…”
MethodInfluentialCESAR: Automatic Induction of Compositional Instructions for Multi-turn Dialogs
Aksu, T., Hazarika, D., Mehri, S., Kim, S., Hakkani-Tür, D. Z., Liu, Y., & Namazifar, M. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.717 · Cited by 1
Machine Reading Comprehension using Case-based Reasoning
Thai, D., Agarwal, D., Chaudhary, M., Zhao, W., Das, R., Lee, J. Y., Hajishirzi, H., Zaheer, M., & McCallum, A. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.564 · Cited by 4
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Controllable Decontextualization of Yes/No Question and Answers into Factual Statements
Mo, L., Fetahu, B., Rokhlenko, O., & Malmasi, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-56060-6_27
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Tonde, M. J., & Sankaye, S. (2024) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-16960
General Words Representation Method for Modern Language Model
Lokman, A. S., Ameedeen, M. A., & Ghani, N. A. (2023) · Journal of Telecommunication Electronic and Computer Engineering (JTEC) · doi:10.54554/jtec.2023.15.01.001
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Liang, Y., Li, L., Liu, R., Ahmat, A., & Jiang, L. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-63751-3_11
“This task has wide-ranging applications in downstream tasks, including question answering [2], information retrieval [3], and causal reasoning [4].”
Learning to generate text with auxiliary tasks
Hùng, P. Q., Nguyen, M.-T., Inoue, S., Tran-Tien, M., & Phan, X.-H. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112570
A Comparative Study on Pre-Trained Models Based on BERT
Zhang, M. (2024) · doi:10.1109/icnlp60986.2024.10692659
“Reference [14] intends to build an English-based dataset containing information-seeking QA dialogues since modeling such dialogues, the highly context-dependent texts, is challenging.”
BackgroundLi, M., Cai, T., Wu, L., Li, C., & Ju, S. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-9431-7_7
Alonso, O., & Baeza-Yates, R. (2024) · ACM eBooks · doi:10.1145/3674127.3674128
Searching Personal Collections
Bendersky, M., Metzler, D., Najork, M., & Wang, X. (2024) · ACM eBooks · doi:10.1145/3674127.3674142
Query-driven Search Functionality
Li, L. (2024) · ACM eBooks · doi:10.1145/3674127.3674133
Alonso, O., & Baeza-Yates, R. (2024) · ACM eBooks · doi:10.1145/3674127.3702962
Alonso, O., & Baeza-Yates, R. (2024) · ACM eBooks · doi:10.1145/3674127.3674129
Alonso, O., & Baeza-Yates, R. (2024) · ACM eBooks · doi:10.1145/3674127.3702961
Hersh, W., & Roberts, K. E. (2024) · ACM eBooks · doi:10.1145/3674127.3702960
Bast, H., Kalmbach, J., Klumpp, T., & Korzen, C. (2024) · ACM eBooks · doi:10.1145/3674127.3674134
Refined Question Reading for Multi-Type Legal Question Answering
Yin, Y., Li, L., Xie, S., Tao, X., & Zhang, J. (2024) · International Conference on Behavioral, Economic, and Socio-Cultural Computing · doi:10.1109/besc64747.2024.10780776
CGM: Copy Mechanism GPT with Mask for Ellipsis and Anaphora Resolution in Dialogue
Cho, J.-W., Oh, J., & Cha, J.-W. (2024) · Applied Sciences · doi:10.3390/app15010005
A Comprehensive Analysis of Pre-trained NLP Models
Kumari, S., Verma, N., Kamble, S. D., & Barekar, P. V. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-8537-7_13
FMIQA: A multimodal integrated QA Robot with considerable intelligence
Xue-zhong, L., Zhang, J., Zhao, Y., Xu, Y., Liu, W., & Zhao, Y. (2024) · International Conference on Electronic Information Technology and Computer Engineering (EITCE) · doi:10.1145/3711129.3711334
“The prototype of recent work focusing on the QA system consists of a textual question and question in a context, and the form of the context may be a table [4], a paragraph [5], an image [6], and so on.”
Imran, A., & Gopalakrishnan, K. (2025) · Apress eBooks · doi:10.1007/979-8-8688-0989-7_4
Small pre-trained model for background understanding in multi-round question answering
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TiconvQA: A Tibetan Conversational Dataset for Text Comprehension
Cairang, P., Cairen, D., & Sun, Y. (2025) · Data Intelligence · doi:10.3724/2096-7004.di.2025.0042
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A Multi-Modal Attentive Framework That Can Interpret Text (MMAT)
Kumari, V., Gupta, S., Sharma, Y., & Goel, L. (2025) · IEEE Access · doi:10.1109/access.2025.3586900 · Cited by 1
“These range from purely text-based question answering, such as SQuAD [1], QuAC [2], and CoQA [3], to more complicated tasks such as Visual Question Answering [4], and even more advanced task is Text-VQA [5].”
Relia, L., Patel, J., Panchal, N. A., & Garg, D. (2025) · Smart innovation, systems and technologies · doi:10.1007/978-981-96-3420-0_17
Zou, Q., & Gong, Y. (2025) · Modelling—International Open Access Journal of Modelling in Engineering Science · doi:10.3390/modelling6030092
“This study utilizes the QuAC (Question Answering in Context) dataset [38], jointly released by New York University and Facebook AI Research in 2018, to advance research on conversational question–answering systems.”
Question Answering in a Low-Resource Language: Dataset and Deep Learning Adaptations for Sinhala
Ranasinghe, J., & Weerasinghe, R. (2025) · Communications in computer and information science · doi:10.1007/978-3-032-04339-9_22
Cao, L., Nguyen, T., Reisert, P., Nichols, E., Maeda, C., Lam, D., Siskind, S. R., & Gómez, R. (2025) · IEEE RO-MAN · doi:10.1109/ro-man63969.2025.11217859
“Inspired by question-answering tasks [12], [13], we adopt a retrieval-augmented generation (RAG) approach to retrieve relevant personality traits only when they are contextually appropriate.”
Moradisani, H., Zarrinkalam, F., Noorian, Z., & Ensan, F. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11421-5
ConvSelect-RAG: Bridging Query Enhancement and Document Filtering for Multi-turn Conversational AI
Dang, K. T., Khac, Q. T., Binh, D. L., & Bao, D. T. N. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-4957-3_31
RBLU: A Benchmark to Evaluate the Reverse Inference Ability of Large Language Models
Wang, H., Wang, F. B., Deng, S., Liu, L., & Liu, X. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-4158-4_6
Knowledge-Graph Structure-Aware Conversational Entity Retrieval
Zamiri, M. (2026) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3773966.3784984
Structured Definitions and Segmentations for Legal Reasoning in LLMs: A Study on Indian Legal Data
Khatri, M., Yusuf, M., Shah, R. R., & Kumaraguru, P. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-15134-6_21
ConvHistQA: exploring comprehensive history selection in conversational question answering
Khan, I., Kumar, A., & Khare, B. K. (2026) · Knowledge and Information Systems · doi:10.1007/s10115-026-02723-3
Analysis and control of online interactions through neural natural language processing
Laugier, L. (2022) · theses.fr (ABES) · doi:10.70675/4743aa15z209az4addz8f29z95150aa230b8
Toofanee, M. S. A. (2023) · theses.fr (ABES) · doi:10.70675/600337cazbfe4z405fz9538za725b88272b7
Proactive models for open-domain conversational search
Erbacher, P. (2024) · theses.fr (ABES) · doi:10.70675/f7d68861zc694z49f1zb130z675cbf38ed1c
Zhang, A., & Lee, M. K. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791306
“To curate a standard set of question cards, we sourced questions from datasheets for datasets which has been frequently used or extended by researchers creating datasheets for their own datasets [22, 55, 120, 133].”
Toward Exploring Mixed-Initiative Conversation Generation Based on Community Question Answering
Lu, L., Kasela, P., Ravenda, F., Meng, C., Pasi, G., & Crestani, F. (2026) · ACM Transactions on Information Systems · doi:10.1145/3814610
“The most common approach to build datasets for ConvQA is crowdsourcing [19, 24, 27, 64, 66], which is different from our idea that focuses on leveraging LLMs to generate conversations with clarifying questions and answers.”
Wu, S., Liu, Y., Gao, Y., Li, Z., Zhang, C. T., Wang, Q., Chen, E., & He, Y. (2026) · Chinese Journal of Electronics · doi:10.23919/cje.2025.00.277
Guan, J., Li, J., Hou, K., & Li, J. (2026) · Complex & Intelligent Systems · doi:10.1007/s40747-026-02342-z
Gagana, K. G., Peddibhotla, C. P., Deepak, P. J., Pasha, F., & Agarwal, P. R. (2026) · Smart innovation, systems and technologies · doi:10.1007/978-3-032-18849-6_15
Naikwade, N., Gadekar, A., Dhopte, S. M., Arbat, R., Hingoliwala, H. A., & Chandre, P. R. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-26905-8_33
ReGAP: Evidence-Aligned Conversational Question Generation via Reasoning-Guided Action Planning
Wang, W., He, L., Zhou, P., & Zheng, W. (2026) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3838193
“Automatic evaluations on three widely used passage-grounded QA and conversational QA resources—CoQA [51], QuAC [12], and SQuAD 2.0 [50]—with multiple large language models, including Mixtral-8 × 7B [32], Qwen3-32B [65], and GPT-4o-mini [1], show that ReGAP consistently improves reasoning quality…”
InfluentialA Review of Deep Learning-Based Question Generation: Challenges, Methods, and Future Directions
Yehia, M. F., Helmi, Y. M., & Bahloul, M. M. (2026) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-026-01497-4
“The natural variation in journalistic writing style and sentence complexity further tests the syntactic and contextual robustness of generated questions, aligning closely with real-world information-seeking behavior [102].”
Persistent Goal-Tracking and Instruction-Driven Reasoning in Sequential Conversation QA
Ngo, Q.-D., Dang, Q.-D., & Phan, L. (2026) · IEEE Access · doi:10.1109/access.2026.3725149
“On the evaluation side, CoQA [39] and QuAC [40] established conversational QA as a benchmark task, but score answer spans rather than whether a business goal was ultimately completed.”
Savant, S., Deshpande, U. U., & D’Silva, J. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01750-x
“Benchmark type, therefore, influences the choice between model architecture and error pattern [40, 41].”
Follow-Up Question Modeling for Open-Retrieval Conversations with WH-Questions
Huang, C.-W., Yen, A.-Z., Huang, H.-H., & Chen, H. (2025) · IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) · doi:10.1109/wi-iat67162.2025.00013
“Datasets like CoQA [12] and QuAC [13] present passages in a dialogue format, featuring multi-turn QA pairs.”
Beyond Context Window: Dynamic Knowledge Graphs for Lifelong Learning Llms
Desai, V., Kalshetty, R., M, M. S., & Kotagi, S. (2026) · IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT) · doi:10.1109/conecct68978.2026.11700592
“We implemented an educational assistant for a simulated student across four subjects over 180 daily sessions, each containing 10–15 QA interactions drawn from QuAC [20], CoQA [21], Natural Questions [22], and a custom educational corpus.”
A topic semantic relational knowledge-enhanced conversational model for improving response accuracy
Li, K., Guo, X., Zhang, S., & Liu, J. (2026) · Knowledge and Information Systems · doi:10.1007/s10115-026-02912-0
“QuAC [46] consists of 100,000 questions from 14,000 information-seeking conversations,posingauniquechallengeduetoitsopen-ended,descriptivequestionsthatrequirefull inference through conversational history.”
InfluentialLu, J., Fang, T., Zhang, T., Wong, D. F., Yang, M., Wang, Y., & Xu, H. (2026) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2026.3742218
“QReCC comprises 13,672 multi-turn dialogues aggregated from TREC CAsT [51], QuAC [52], and Google Natural Questions [4], characterized by implicit context dependence where queries frequently exhibit el-lipses,pronounreferences,orambiguousexpressionsnecessitat-inghistoricalresolution.”
Efficient Benchmarking (of Language Models)
Perlitz, Y., Bandel, E., Gera, A., Arviv, O., Ein-Dor, L., Shnarch, E., Slonim, N., Shmueli-Scheuer, M., & Choshen, L. (2023) · arXiv · doi:10.48550/arxiv.2308.11696
Grattafiori, A., Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A., Mathur, A., Schelten, A., Vaughan, A., et al. (2024) · arXiv · doi:10.48550/arxiv.2407.21783 · Cited by 505
SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems
Wang, A., Pruksachatkun, Y., Nangia, N., Singh, A., Michael, J., Hill, F., Levy, O., & Bowman, S. R. (2019) · arXiv · doi:10.48550/arxiv.1905.00537 · Cited by 1,051
“It was challenging to train annotators to do well on Quora Insincere Questions 10 , Empa-thetic Reactions (Buechel et al., 2018), and a recast version of Ultra-Fine Entity Typing (Choi et al., 2018b, see Appendix C.3 for details), leading to low human performance.”
BackgroundMethodInfluential
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.
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