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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
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Research works printed but not linked: 3
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Items 301–400 of 704
OPERA: Harmonizing Task-Oriented Dialogs and Information Seeking Experience
Li, M., Peng, B., Gao, J., & Zhang, Z. (2023) · ACM Transactions on the Web · doi:10.1145/3623381 · Cited by 14
“Previous work on ConvQA has mainly focused on two categories [14]: conversational machine comprehension ( CMC ) task [10, 50, 72] and sequential knowledge-base question answering ( KB-QA ).”
Unifying Text, Tables, and Images for Multimodal Question Answering
Luo, H., Shen, Y. J., & Deng, Y. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.626 · Cited by 11
“In typical question answering (QA) systems, answers are typically derived from a single modality, such as an image (Antol et al., 2015), passage (Choi et al., 2018), or table (Oguz et al., 2022), without the need for cross-modality reasoning.”
BackgroundMemory Graph Networks for Explainable Memory-grounded Question Answering
Moon, S., Shah, P., Kumar, A., & Subba, R. (2019) · Conference on Computational Natural Language Learning · doi:10.18653/v1/k19-1068 · Cited by 11
“Several recent work address multi-hop reasoning within multiple documents (Yang et al., 2018; Welbl et al., 2018; Bauer et al., 2018; Clark et al., 2018) or conversational settings (Choi et al., 2018; Reddy et al., 2018), which require often complex reasoning tools.”
BackgroundOpen-Domain Conversational Search Assistant with Transformers
Ferreira, R., Leite, M., Semedo, D., & Magalhães, J. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-72113-8_9 · Cited by 15
Conversations with Search Engines.Preprint
Ren, P., Chen, Z., Ren, Z., Kanoulas, E., Monz, C., & de Rijke, M. (2020) · arXiv (Cornell University) · Cited by 10
A Survey of Document Grounded Dialogue Systems (DGDS)Preprint
Ma, L., Zhang, W., Li, M., & Liu, T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.13818 · Cited by 15
“urce, etc. In Table2, we list the differences not only between the DGDS and the3 DS categories, but also between the CRC, the DBD, and the MRC. 3 ARCHITECTURE Most recently, a number of DGDS datasets [11, 29, 66, 73, 81, 83, 118, 138] and DGDS models [3, 8, 9, 31, 36, 39, 48, 55, 62, 68, 75, 76, 82, 91, 95, 100, 126,…”
BackgroundMethodInfluentialMore Than Reading Comprehension: A Survey on Datasets and Metrics of Textual Question AnsweringPreprint
Bai, Y., & Wang, D. Z. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.12264 · Cited by 19
“QuAC[11] 98K 84K 7K 7K 9K Unique Section 397”
BackgroundMethodInfluentialGekhman, Z., Oved, N., Keller, O., Szpektor, I., & Reichart, R. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00549 · Cited by 9
“Since QuAC-NH contains incorrect answers, if a k is incorrect we discard E k to avoid corrupting the evaluation set with incorrectly labeled examples.”
BackgroundMethodInfluentialAdding Error Bars to Evals: A Statistical Approach to Language Model Evaluations
Miller, E. (2024) · arXiv · doi:10.48550/arxiv.2411.00640 · Cited by 9
“For instance, DROP[6], QuAC[5], RACE[13], and SQuAD[17] are reading-comprehension evals having multiple related questions about independently selected passages of text, and multilingual evals such as MGSM[18] consist of the same question translated into many languages.”
BackgroundDiscourse Comprehension: A Question Answering Framework to Represent Sentence Connections
Ko, W.-J., Dalton, C., Simmons, M., Fisher, E., Durrett, G., & Li, J. J. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.806 · Cited by 10
ProCIS: A Benchmark for Proactive Retrieval in Conversations
Samarinas, C., & Zamani, H. (2024) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3626772.3657869 · Cited by 16
“Popular conversational question answering benchmarks, such as QuAC [8] and CoQA [30], and TREC Conversational Assistance Track (CAsT) [25] have been mainly focusing on evaluating CIS systems based on such paradigm.”
BackgroundCastells, P., & Jannach, D. (2024) · ACM eBooks · doi:10.1145/3674127.3674131 · Cited by 10
Mucciaccia, S. S., Paixão, T. M., Mutz, F. W., Souza, A. F. D., Badue, C. S., & Oliveira-Santos, T. (2025) · Journal of the Brazilian Computer Society · doi:10.5753/jbcs.2025.5801 · Cited by 10
Zaib, M., Sheng, Q. Z., Zhang, W. E., & Mahmood, A. (2023) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn54540.2023.10191510 · Cited by 12
“1) QuAC: Question Answering in Context (QuAC) [2] consists of 100k question-answer pairs in a teacher-student information-seeking setting.”
BackgroundMethodInfluentialKongyoung, S., Macdonald, C., & Ounis, I. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.485 · Cited by 10
“OR-QuAC: This dataset has been introduced by Qu et al. (2020), adapting the well-known QuAC (Choi et al., 2018) dataset to an open-retrieval setting.”
BackgroundMethodInfluentialDirectional attention weaving for text-grounded conversational question answering
Li, R., Jiang, Z., Wang, L., Lu, X., & Zhao, M. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.01.056 · Cited by 9
Comparative Analysis of Transformer based Language Models
Pathak, A. (2021) · doi:10.5121/csit.2021.110111 · Cited by 10
“On the contrary, it performs notably dull on natural language inference (NLI) tasks such as ANLI [26], WIC [27] and on many reading comprehension tasks such as DROP [28], MultiRC [29], RACE [13] and QuAC [30].”
BackgroundCan Question Rewriting Help Conversational Question Answering?
Ishii, E., Xu, Y., Cahyawijaya, S., & Wilie, B. (2022) · Workshop on Insights from Negative Results in NLP (Insights) · doi:10.18653/v1/2022.insights-1.13 · Cited by 11
Tracing Origins: Coreference-aware Machine Reading Comprehension
Huang, B., Zhang, Z., & Zhao, H. Y. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.91 · Cited by 10
“(Harman, 1993), SQuAD (Rajpurkar et al., 2018), NewsQA (Trischler et al., 2017), SearchQA (Dunn et al., 2017), and QuAC (Choi et al., 2018), and intensive efforts were made to build new models that surpass the human performance on these datasets, including the pre-trained language models (Devlin et…”
BackgroundMethodInfluentialGoal Awareness for Conversational AI: Proactivity, Non-collaborativity, and Beyond
Deng, Y., Lei, W., Huang, M., & Chua, T. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-tutorials.1 · Cited by 10
“…dialogue (TOD) systems (Budzianowski et al., 2018; Lei et al., 2018; Su et al., 2022), conversational question answering (CQA) systems (Choi et al., 2018; Reddy et al., 2019; Anantha et al., 2021; Qiu et al., 2021), and conversational recommender systems (CRS) (Li et al., 2018; Deng…”
BackgroundDG2: Data Augmentation Through Document Grounded Dialogue Generation
Wu, Q., Feng, S., Chen, D., Joshi, S., Lastras, L., & Zhou, Y. (2022) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/2022.sigdial-1.21 · Cited by 10
MCˆ2: Multi-perspective Convolutional Cube for Conversational Machine Reading Comprehension
Zhang, X. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1622 · Cited by 9
“, 2019) and QuAC (Choi et al., 2018), are proposed to enable models to understand passages and answer questions in dialogue.”
BackgroundEmploying Abstract Meaning Representation to Lay the Last-Mile Toward Reading Comprehension
Galitsky, B. A. (2020) · Human-computer interaction series · doi:10.1007/978-3-030-52167-7_3 · Cited by 9
Conversational Search and Recommendation: Introduction to the Special Issue
Hauff, C., Kiseleva, J., Sanderson, M., Zamani, H., & Zhang, Y. (2021) · ACM Transactions on Information Systems · doi:10.1145/3465272 · Cited by 12
“…Fu et al. [2020] and Gao et al. [2018]), shared tasks (e.g., Dalton et al. [2019, 2020], Ram et al. [2018]), and datasets (e.g., Aliannejadi et al. [2019], Choi et al. [2018], Radlinski et al. [2019], Reddy et al. [2019], Thomas et al. [2017], Trippas and Thomas [2019], and Zamani et al. [2020b]).”
BackgroundMTRAG: A Multi-Turn Conversational Benchmark for Evaluating Retrieval-Augmented Generation SystemsPreprint
Katsis, Y., Rosenthal, S., Fadnis, K., Gunasekara, C., Lee, Y., Popa, L., Shah, V., Zhu, H., Contractor, D., & Danilevsky, M. (2025) · Qeios · doi:10.32388/phs6yn · Cited by 9
Using Model Cards for ethical reflection on machine learning models: an interview-based study
Nunes, J. L., Barbosa, G. D. J., de Souza, C. S., & Barbosa, S. D. J. (2024) · Journal on Interactive Systems · doi:10.5753/jis.2024.3444 · Cited by 8
“Miceli et al. [2021] conducted a study in the field of computer vision to compare these general proposals and ones made specifically for the publication of certain datasets [Choi et al., 2018; Seck et al., 2018].”
BackgroundA Survey on LLMs: Evolution, Applications, and Future Frontiers
Harsha, K., Kumar, K. T., Sumathi, D., & Jubilson, E. A. (2024) · Studies in computational intelligence · doi:10.1007/978-981-97-8460-8_14 · Cited by 11
Question Answering over Curated and Open Web Sources
Roy, R. S., & Anand, A. (2020) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3397271.3401421 · Cited by 8
“erstandthecontextleftimplicitbyusersandeffectivelyanswerincompleteandadhocfollow-upquestions.Towardsthis,various recent benchmarks have been proposed that expect answers thatare boolean[69],extractive [18]and free-form responses [68], entities [19], passages [48], and chit-chat [101]. Leaderboards of the QuAC [18] and…”
BackgroundInfluentialEvaluation Paradigms in Question Answering
Rodríguez, P., & Boyd-Graber, J. L. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.758 · Cited by 12
Dietz, L., & Dalton, J. (2020) · Datenbank-Spektrum · doi:10.1007/s13222-020-00334-y · Cited by 8
“Similar benchmarks can be derived from chatbot dialog collections [19].”
BackgroundHierarchical Dependence-aware Evaluation Measures for Conversational Search
Faggioli, G., Ferrante, M., Ferro, N., Perego, R., & Tonellotto, N. (2021) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3404835.3463090 · Cited by 10
Towards a more Robust Evaluation for Conversational Question Answering
Siblini, W., Sayil, B., & Kessaci, Y. (2021) · doi:10.18653/v1/2021.acl-short.130 · Cited by 8
“For instance, models like BERT (Devlin et al., 2019; Lan et al., 2019; Sanh et al., 2019) obtain a more than decent performance on CQA datasets like QuAC (Choi et al., 2018) or CoQA (Reddy et al., 2019).”
BackgroundMethodInfluentialCSAGN: Conversational Structure Aware Graph Network for Conversational Semantic Role Labeling
Wu, H., Xu, K., & Song, L. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.177 · Cited by 8
“…improvements on conversation-based tasks, such as dialogue response generation (Li et al., 2017; Dinan et al., 2019; Wu et al., 2019), task-oriented dialogue modeling (Mrkši´c et al., 2017; Budzianowski et al., 2018) and conversational question answering (Choi et al., 2018; Reddy et al., 2019).”
BackgroundYadav, S., & Kaushik, A. (2022) · Knowledge · doi:10.3390/knowledge2010004 · Cited by 8
Interpretable modular knowledge reasoning for machine reading comprehension
Ren, M., Huang, H., & Gao, Y. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-06975-2 · Cited by 8
“mances to those of humans on several datasets [11, 14, 56, 69].”
BackgroundExternal Knowledge Acquisition for End-to-End Document-Oriented Dialog Systems
Lai, T., Castellucci, G., Kuzi, S., Ji, H., & Rokhlenko, O. (2023) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.264 · Cited by 9
“Document-oriented Conversations Humans typically seek information in a conversational manner, for example to find answers to questions (Choi et al., 2018) or to seek guidance in performing real world tasks (Gottardi et al.”
BackgroundMethodCLER: Cross-task Learning with Expert Representation to Generalize Reading and Understanding
Takahashi, T., Taniguchi, M., Taniguchi, T., & Ohkuma, T. (2019) · Workshop on Machine Reading for Question Answering (MRQA) · doi:10.18653/v1/d19-5824 · Cited by 9
“…of RC task, such as multiple passage (Dunn et al., 2017; Joshi et al., 2017), multi-hop reasoning (Yang et al., 2018; Welbl et al., 2018), dialog (Choi et al., 2018; Reddy et al., 2019) and commonsense reasoning (Ostermann et al., 2018; Talmor et al., 2019 To assess the performance of an RC…”
BackgroundHashemi, H., Zamani, H., & Croft, W. B. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.07548 · Cited by 8
“rg/10.1145/3397271.3401061 Similarly, conversational question answering based on a set of related questions about a given passage has been explored in the natural language processing (NLP) literature [7, 40, 44]. However, the existing settings are still far from the ideal mixed-initiative scenario, in which both user…”
BackgroundInfluentialChallenges in Information-Seeking QA: Unanswerable Questions and Paragraph RetrievalPreprint
Asai, A., & Choi, E. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.11915 · Cited by 8
“However, those state-of-the-art models have difficulty answering information-seeking questions (Kwiatkowski et al., 2019; Choi et al., 2018).”
BackgroundSelf-citationG4: Grounding-guided Goal-oriented Dialogues Generation with Multiple Documents
Zhang, S., Du, Y., Liu, G., Zhao, Y., & Cao, Y. (2022) · DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering (DialDoc) · doi:10.18653/v1/2022.dialdoc-1.11 · Cited by 9
“Most previous works have focused on single-document conversational question answering tasks, such as QuAC(Choi et al., 2018), CoQA(Reddy et al., 2019), Doc2Dial(Feng et al., 2020).”
BackgroundGenerating Information-Seeking Conversations from Unlabeled Documents
Kim, G., Kim, S., Yoo, K. M., & Kang, J. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.151 · Cited by 11
“Following Choi et al. (2018), we collect Wikipedia articles from a list of category keywords leveraging a web interface, Wikipedia foundation 8 .”
BackgroundMethodInfluentialCampos, J. A., Cho, K., Otegi, A., Soroa, A., Agirre, E., & Azkune, G. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.230 · Cited by 9
Bridging Speech and Textual Pre-Trained Models With Unsupervised ASR
Shi, J., Hsu, C.-J., Chung, H.-L., Gao, D., Garcia, L. P., Watanabe, S., Lee, A., & Lee, H.-Y. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10096827 · Cited by 9
“Three test sets included are derived from SQuAD [33], NEWSQA [34] and QuAC [35], whereas the latter two are reported together as out-of-domain (OOD) samples.”
MethodInteractive Language Learning by Question AnsweringPreprint
Yuan, X., Côté, M.-A., Fu, J., Lin, Z., Pal, C., Bengio, Y., & Trischler, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.10909 · Cited by 13
“Reddy et al. (2018); Choi et al. (2018) explore conversational question answering, in which a full understanding of the question depends on the conversation’s history.”
BackgroundZhang, Y., Dong, X., Chang, S., Yu, T., Shi, P., & Zhang, R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.12634 · Cited by 26
“Alexa, Natural Questions (NQ), MARCO, WikiQA, CoQA, QuAC contains chit-chat questions (or statements) of passages, concepts, documents, and web searches instead of relational databases (Gopalakrishnan et al., 2019; Kwiatkowski et al., 2019; Nguyen et al., 2016; Yang et al., 2015; Reddy et al., 2019; Choi et al., 2018).”
DEEP LEARNING FOR SEMANTIC MATCHING: A SURVEY
Li, H., Govind, Y., Mudgal, S., Rekatsinas, T., & Doan, A. (2021) · Journal of Computer Science and Cybernetics · doi:10.15625/1813-9663/37/4/16151 · Cited by 7
“Recent work has also examined machine reading comprehension [14, 55], which answers a question given a document (e.g., a description on an object, a narrative story, or even a dialog between two persons).”
BackgroundInSCIt : Information-Seeking Conversations with Mixed-Initiative Interactions
Wu, Z., Parish, R., Cheng, H., Min, S., Ammanabrolu, P., Mari Ostendorf, E., & Hajishirzi, H. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00559 · Cited by 23
“Early CQA work including QuAC (Choi et al., 2018) and CoQA (Reddy et al., 2019) requires the agent to answer each user question in a conversation by reading a short passage.”
BackgroundMethodInfluentialCQuAE: A new Contextualized QUestion Answering corpus on Education domain
Gerald, T., Tamames, L., Ettayeb, S., Le, H.-Q., Paroubek, P., & Vilnat, A. (2024) · Data & Knowledge Engineering · doi:10.1016/j.datak.2024.102305 · Cited by 6
Seo, J., Lee, T., Moon, H., Park, C., Eo, S., Aiyanyo, I. D., Park, K., So, A., Ahn, S., & Park, J. (2022) · Mathematics · doi:10.3390/math10081335 · Cited by 7
Zero-shot Query Reformulation for Conversational Search
Yang, D., Zhang, Y., & Fang, H. (2023) · ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR) · doi:10.1145/3578337.3605143 · Cited by 6
“Consequently,Machine reading comprehension (MRC) is a promising area for this purpose, as it offers a highly flexible question format and a wealth of large-scale datasets such as SQuAD, SQuAD2, and QuAC [13, 12, 1].”
BackgroundJeong, S., Baek, J., Hwang, S. J., & Park, J. C. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.374 · Cited by 6
Fan, A. X., Zhang, H., Paquette, L., & Zhang, R. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.496 · Cited by 12
Masumura, R., Ihori, M., Tanaka, T., Saito, I., Nishida, K., & Oba, T. (2019) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru46091.2019.9003857 · Cited by 6
“Reading comprehension: Global-context iterative rescoring for GLCLMs is a task similar to reading comprehension in which, given the input of passage sentences and a question sentence, an answer sentence is output [32–34].”
BackgroundCommonsense Knowledge Adversarial Dataset that Challenges ELECTRA
Lin, G., Miao, Y., Yang, X., Ou, W., Cui, L., Guo, W., & Miao, C. (2020) · International Conference on Control, Automation, Robotics and Vision (ICARCV) · doi:10.1109/icarcv50220.2020.9305451 · Cited by 6
“In recent years, many large-scale datasets have been developed, such as SQuAD 1.1 [22], TriviaQA [23], QuAC[21], and COMMENSENQA[16].”
InfluentialContextual and Semantic Fusion Network for Multiple-Choice Reading Comprehension
Duan, Q., Huang, J., & Wu, H. (2021) · IEEE Access · doi:10.1109/access.2021.3068993 · Cited by 6
“The answers of abstract MRC datasets are free-generated by human beings based on given passage (e.g., MS MARCO [22], QuAC [23], CoQA [24]).”
BackgroundTowards more equitable question answering systems: How much more data do you need?
Debnath, A., Rajabi, N., Alam, F. F., & Anastasopoulos, A. (2021) · doi:10.18653/v1/2021.acl-short.79 · Cited by 8
“The effort initially focused solely on English, with datasets like WikiQA (Yang et al., 2015), MS MARCO (Nguyen et al., 2016), SQuAD (Ra-jpurkar et al., 2016), QuAC (Choi et al., 2018), CoQA (Reddy et al., 2019), and Natural Questions (NQ) (Kwiatkowski et al., 2019 research (Joshi et al., 2020), larger efforts have pr…”
BackgroundIshii, E., Wilie, B., Xu, Y., Cahyawijaya, S., & Fung, P. (2022) · Annual Meeting of the Association for Computational Linguistics: Student Research Workshop (ACL SRW) · doi:10.18653/v1/2022.acl-srw.6 · Cited by 6
“It has been widely studied in the recent years and many CQA datasets have been made publicly available, such as CoQA (Reddy et al., 2019), QuAC (Choi et al., 2018), ShARC(Saeidi et al., 2018), Doc2Dial (Feng et al., 2020), and DoQA (Campos et al., 2020).”
BackgroundMethodInfluentialStrathearn, C., & Gkatzia, D. (2022) · DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering (DialDoc) · doi:10.18653/v1/2022.dialdoc-1.21 · Cited by 8
Capturing Conversational Interaction for Question Answering via Global History Reasoning
Qian, J., Zou, B., Dong, M., Li, X., Aw, A. T., & Yu, H. Z. (2022) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2022.findings-naacl.159 · Cited by 6
“Besides, a few studies argue that historical answer spans in context are more crucial, so they mark the answers when encoding the context (Choi et al., 2018; Qu et al., 2019a; Ohsugi et al., 2019; Qu et al., 2019b).”
BackgroundMethodInfluentialImproving conversational search with query reformulation using selective contextual history
Al-Thani, H., Elsayed, T., & Jansen, B. J. J. (2022) · Data and Information Management · doi:10.1016/j.dim.2022.100025 · Cited by 6
Machine reading comprehension combined with semantic dependency for Chinese zero pronoun resolution
Bi, M., Liu, X., Zhang, Q., & Yang, Z. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10364-5 · Cited by 6
“2016), question answering system (Choi et al. 2018; Reddy et al. 2019; Sun et al. 2019; Bi et al. 2021, 2020), information extraction (Hemmatian and Sohrabi 2019; Rana and Cheah 2016), and other applications (Chen et al.”
BackgroundCONSISTENT: Open-Ended Question Generation From News Articles
Chakrabarty, T., Lewis, J., & Muresan, S. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.517 · Cited by 6
AutoConv: Automatically Generating Information-seeking Conversations with Large Language Models
Li, S., Yang, C., Yin, Y., Zhu, X., Cheng, Z., Shang, L., Jiang, X., Liu, Q., & Yang, Y. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-short.149 · Cited by 11
“We conduct experiments on QuAC (Choi et al., 2018) and CoQA (Reddy et al.”
BackgroundMethodInfluentialQuery Refinement Prompts for Closed-Book Long-Form QA
Amplayo, R. K., Webster, K., Collins, M., Das, D., & Narayan, S. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.444 · Cited by 23
“Since, there has been a proliferation of reading comprehension datasets developed which probe for specific capabilities (Joshi et al., 2017; Choi et al., 2018; Reddy et al., 2019; Rodriguez et al., 2019).”
BackgroundMcQueen: a Benchmark for Multimodal Conversational Query Rewrite
Yuan, Y., Chen, S., Wang, R., Chen, L., Jiang, F., You, Y., & Lam, W. P. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.320 · Cited by 7
Learning to Select the Relevant History Turns in Conversational Question Answering
Zaib, M., Zhang, W. E., Sheng, Q. Z., Sagar, S., Mahmood, A., & Zhang, Y. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-7254-8_26 · Cited by 6
Multi-turn dialogue comprehension from a topic-aware perspective
Ma, X., Xu, Y., Zhao, H., & Zhang, Z. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127385 · Cited by 7
Kaiser, M., Roy, R. S., & Weikum, G. (2024) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3616855.3635822 · Cited by 6
“This work contributes a method that makes conversational question answering models more robust with generated reformulations that are specifically guided towards better QA performance.”
MethodYTCommentQA: Video Question Answerability in Instructional Videos
Yang, S., Park, S., Jang, Y., & Lee, M. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i17.29906 · Cited by 11
Question-Answering Dialog System for Large Audiovisual Archives
Chýlek, A., Šmídl, L., & Švec, J. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-27947-9_33 · Cited by 6
HisBERT for Conversational Reading Comprehension
Liu, C., Xiong, D., Jia, Y., Zan, H., & Hu, C. (2020) · International Conference on Asian Language Processing · doi:10.1109/ialp51396.2020.9310487 · Cited by 6
“• BiDAF++ w/2-Context [5]: Unlike BiDAF++, it considers merging conversation history, and the number 2 represents the merging of two historical rounds.”
BackgroundMethodInfluentialSituation and Behavior Understanding by Trope Detection on Films
Chang, C.-H., Su, H.-T., Hsu, J.-H., Wang, Y.-S., Chang, Y., Liu, Z. Y., Chang, Y.-L., Cheng, W., Wang, K.-J., & Hsu, W. H. (2021) · International World Wide Web Conference (WWW) · doi:10.1145/3442381.3449806 · Cited by 7
“Cognitive science research [12] also suggests that, comparing to expository text, which is utilized for building various machine comprehension datasets [4, 30, 38, 40], narrative text has a close correspondence to everyday experiences in contextually specific situations.”
BackgroundRole Aware Multi-Party Dialogue Question Answering
Hsu, J.-H., Shen, P.-W., Su, H.-T., Chang, C.-H., Yeh, J.-F., & Hsu, W. H. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414980 · Cited by 6
“CoQA [13] and QuAC [14] make question answering in a conversational form.”
BackgroundBuilding Goal-oriented Document-grounded Dialogue Systems
Chen, X., Lin, F., Zhou, Y., Ma, K., Francis, J., Nyberg, E., & Oltramari, A. (2021) · doi:10.18653/v1/2021.dialdoc-1.14 · Cited by 6
“, 2020), while, for others, the dialogue and question-answer pairs are based on a document (Choi et al., 2018).”
BackgroundQuestion Rewriting for Open-Domain Conversational QA
Tredici, M. D., Barlacchi, G., Shen, X., Cheng, W., & de Gispert, A. (2021) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3459637.3482164 · Cited by 6
“QReCC combines questions from QuAC [2], TREC CAsT [3], and Google NQ [9], with the underlying corpus containing 54M passages from CommonCrawl.”
BackgroundMethodInfluentialLess Is More: Domain Adaptation with Lottery Ticket for Reading Comprehension
Zhu, H., Wang, Z., Zhang, H., Liu, M., Zhao, S., & Qin, B. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.95 · Cited by 7
Iconary: A Pictionary-Based Game for Testing Multimodal Communication with Drawings and Text
Clark, C., Salvador, J., Schwenk, D., Bonafilia, D., Yatskar, M., Kolve, E., Herrasti, A., Choi, J., Mehta, S., Skjonsberg, S., et al. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.141 · Cited by 6
“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-citationOpen-domain conversational search assistants: the Transformer is all you need
Ferreira, R., Leite, M., Semedo, D., & Magalhães, J. (2022) · Information Retrieval · doi:10.1007/s10791-022-09403-0 · Cited by 6
“It was created by manually rewriting the queries in QuAC (Choi et al. 2018) to form non-conversa-tional queries.”
BackgroundMethodCookDial: a dataset for task-oriented dialogs grounded in procedural documents
Jiang, Y., Zaporojets, K., Deleu, J., Demeester, T., & Develder, C. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03692-0 · Cited by 7
“In this area, recently various datasets have been released, including QuAC [8], CoQA [7], DoQA [9] and ShARC [10] .”
BackgroundMethodInfluentialAsking the Right Question: Inferring Advice-Seeking Intentions from Personal Narratives
Fu, L., Chang, J. P., & Danescu-Niculescu-Mizil, C. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1052 · Cited by 9
“Conversational QA datasets (Choi et al., 2018; Reddy et al., 2019) have been introduced to help develop systems with such capability.”
BackgroundTowards Interpretable Reasoning over Paragraph Effects in Situation
Ren, M., Geng, X., Qin, T., Huang, H., & Jiang, D. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.548 · Cited by 6
Model-based annotation of coreferencePreprint
Aralikatte, R., & Søgaard, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.10724 · Cited by 6
“For QuAC documents, where all links are scrubbed, we parse their original Wikipedia pages to get the entities.”
InfluentialProceedings of the First Workshop on NLP for Conversational AI
(2019) · doi:10.18653/v1/w19-41 · Cited by 7
Complex QA and language models hybrid architectures, SurveyPreprint
Daull, X., Bellot, P., Bruno, E., Martin, V., & Murisasco, É. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2302.09051 · Cited by 32
Enhancing long-form question answering via reflection with question decomposition
Xiao, J., Wu, W., Zhao, J., Fang, M., & Wang, J. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2025.104274 · Cited by 6
Question Answering Systems Based on Pre-trained Language Models: Recent Progress
Luo, X., Luo, Y., & Yang, B. (2024) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-57808-3_13 · Cited by 6
GL-NER: Generation-Aware Large Language Models for Few-Shot Named Entity Recognition
Zhu, X., Dai, F., Gu, X., Li, B., Zhang, M., & Wang, W. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72350-6_30 · Cited by 8
Maity, S., Deroy, A., & Sarkar, S. (2024) · SN Computer Science · doi:10.1007/s42979-024-03464-2 · Cited by 7
“The current state-of-the-art QG approaches also took advantage of the existence of extensive QA datasets, including the Natural Questions corpus, QuAC [34], TriviaQA, NewsQA, QG-STEC, etc.”
NUT-RC: Noisy User-generated Text-oriented Reading Comprehension
Huang, R., Zou, B., Hong, Y., Zhang, W., Aw, A. T., & Zhou, G. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.242 · Cited by 5
“Besides, to make RC models have the ability of conversation understanding like a human being, more challenging multi-round conversational RC datasets are proposed, such as QuAC (Choi et al., 2018) and CoQA (Reddy et al.”
EviDR: Evidence-Emphasized Discrete Reasoning for Reasoning Machine Reading Comprehension
Zhou, Y., Bao, J., Sun, H., Liang, J., Wu, Y., He, X., Zhou, B., & Zhao, T. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-88480-2_35 · Cited by 5
Crowdsourcing Beyond Annotation: Case Studies in Benchmark Data Collection
Suhr, A., Vania, C., Nangia, N., Sap, M., Yatskar, M., Bowman, S. R., & Artzi, Y. (2021) · doi:10.18653/v1/2021.emnlp-tutorials.1 · Cited by 6
“It has been applied to a plethora of tasks, including question answering (Rajpurkar et al., 2016; Choi et al., 2018), textual entailment (Williams et al., 2018; Khot et al., 2018), instruction following (Bisk et al., 2016; Misra et al., 2018; Suhr et al., 2019a; Chen et al., 2019a), visual…”
BackgroundMethodSelf-citationQuestion Difficulty Estimation Based on Attention Model for Question Answering
Song, H.-J., Yoon, S.-H., & Park, S.-B. (2021) · Applied Sciences · doi:10.3390/app112412023 · Cited by 5
Li, K., Zhang, T., Tang, L., Li, J., Lu, H., Wu, X., & Meng, H. M. L. (2022) · DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering (DialDoc) · doi:10.18653/v1/2022.dialdoc-1.13 · Cited by 5
“Other work like QuAC (Choi et al., 2018), ShARC (Saeidi et al.”
MethodCogNLG: Cognitive graph for KG‐to‐text generation
Lai, P., Ye, F., Fu, Y.-G., Chen, Z., Wu, Y., Wang, Y., & Chang, V. I. C. (2023) · Expert Systems · doi:10.1111/exsy.13461 · Cited by 5
Ajawan, P., Desai, V., Kale, S., & Patil, S. (2024) · Journal of The Institution of Engineers (India) Series B · doi:10.1007/s40031-023-00952-6 · Cited by 5
ILPS at TREC 2019 Conversational Assistant Track.
Voskarides, N., Li, D., Panteli, A., & Ren, P. (2019) · Text REtrieval Conference · Cited by 5
FocusQA: Open-Domain Question Answering with a Context in Focus
Barlacchi, G., Lauriola, I., Moschitti, A., Tredici, M. D., Shen, X., Vu, T., Byrne, B., & de Gispert, A. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.381 · Cited by 5
“…influenced the creation of CQA datasets leveraging a wider forms of context such as newspapers (Trischler et al., 2017), web pages (Chen et al., 2021), visual information (Zhu et al., 2016; Biten et al., 2019), and conversational history (Choi et al., 2018; Qu et al., 2020; Anantha et al., 2021).”
BackgroundInfluentialContinually Improving Extractive QA via Human Feedback
Gao, G., Chen, H., Artzi, Y., & Choi, E. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.27 · Cited by 10
Self-citationCrowd-sourcing annotation of complex NLU tasks: A case study of argumentative content annotation
Lavee, T., Kotlerman, L., Orbach, M., Bilu, Y., Jacovi, M., Aharonov, R., & Slonim, N. (2019) · doi:10.18653/v1/d19-5905 · Cited by 5
“The availability and scale of crowdsourcing platforms today has enabled the collection of large scale labeled datasets (Negri et al., 2011; Sabou et al., 2014; Rajpurkar et al., 2016, 2018; Choi et al., 2018).”
BackgroundSupporting search engines with knowledge and context
Voskarides, N. (2021) · ACM SIGIR Forum · doi:10.1145/3527546.3527573 · Cited by 5
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.
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