The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
QuAC: Question Answering in Context
Open accessAbstract
We present QuAC, a dataset for Question Answering in Context that contains 14K information-seeking QA dialogs (100K questions in total).The dialogs involve two crowd workers: (1) a student who poses a sequence of freeform questions to learn as much as possible about a hidden Wikipedia text, and (2) a teacher who answers the questions by providing short excerpts from the text.QuAC introduces challenges not found in existing machine comprehension datasets: its questions are often more open-ended, unanswerable, or only meaningful within the dialog context, as we show in a detailed qualitative evaluation.We also report results for a number of reference models, including a recently state-ofthe-art reading comprehension architecture extended to model dialog context.Our best model underperforms humans by 20 F1, suggesting that there is significant room for future work on this data.Dataset, baseline, and leaderboard available at http://quac.ai.How was perversion handled?How long was he there?How popular did she become?How did Mark Felt contact Woodword?How did the meeting go?How did it do on the charts?When was she born?When was it founded?When was the breakup?
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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.
Cited by
Sorted by citations
Items 101–200 of 704
Introducing MANtIS: a novel Multi-Domain Information Seeking Dialogues DatasetPreprint
Penha, G., Bălan, A. O., & Hauff, C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.04639 · Cited by 75
“MultiWOZ [4] EMNLP NLP 8,438 3 3 3 3 QuAC [8] EMNLP NLP 13,594 3 3 a 3 3 WoW [11] ICLR ML 22,311 3 3 3 3”
What do Models Learn from Question Answering Datasets?
Sen, P., & Saffari, A. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.190 · Cited by 62
“…is in large part due to the release of large language models like BERT (Devlin et al., 2019) and new datasets that have introduced impossible questions (Rajpurkar et al., 2018), bigger scales (Kwiatkowski et al., 2019), and context (Choi et al., 2018; Reddy et al., 2019) to question answering.”
BackgroundMethodDoQA - Accessing Domain-Specific FAQs via Conversational QA
Campos, J. A., Otegi, A., Soroa, A., Deriu, J., Cieliebak, M., & Agirre, E. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.652 · Cited by 59
Deep learning based question answering system in Bengali
Mayeesha, T. T., Sarwar, A. M., & Rahman, R. M. (2020) · Journal of Information and Telecommunication · doi:10.1080/24751839.2020.1833136 · Cited by 54
“From 2015 – 2016, there have been multiple attempts at curating large scale question answering datasets for English such as CNN/ Daily Mail Corpus (Hermann et al., 2015), SQuAD 1.0 (Rajpurkar et al., 2016), RACE (Lai et al., 2017), SQuAD 2.0 (Rajpurkar et al., 2018), QuAC (Choi et al., 2015), CoQA (Reddy et al., 2018)…”
BackgroundInfluentialConversations with Search Engines: SERP-based Conversational Response Generation
Ren, P., Chen, Z., Ren, Z., Kanoulas, E., Monz, C., & de Rijke, M. (2021) · ACM Transactions on Information Systems · doi:10.1145/3432726 · Cited by 68
“l datasets on search, chitchat and question answer. Dataset Multiple turns Complex query Open domain Conversational response CAsT [12] ✓ ✓ ✓ ✗ Holl-e [23], WoW [13] ✓ ✓ ✗ ✓ MS MARCO [24] ✗ ✓ ✓ ? QuAC [10], CoQA [30] ✓ ✗ ✗ ✗ SaaC (this paper) ✓ ✓ ✓ ✓ conversational queries by introducing coherent transitions, e.g., cor…”
InfluentialEfficient Meta Lifelong-Learning with Limited Memory
Wang, Z., Mehta, S. V., Póczos, B., & Carbonell, J. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.39 · Cited by 57
Kaiser, M., Roy, R. S., & Weikum, G. (2021) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3404835.3462859 · Cited by 50
“3462859 satisfying information needs that are too complex or exploratory to be formulated in a single shot [11, 23, 30, 47, 50, 52].”
BackgroundCONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning
Wu, Z., Luan, Y., Rashkin, H., Reitter, D., Hajishirzi, H., Mari Ostendorf, E., & Tomar, G. S. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.679 · Cited by 73
Analysing the Effect of Clarifying Questions on Document Ranking in Conversational Search
Krasakis, A. M., Aliannejadi, M., Voskarides, N., & Kanoulas, E. (2020) · ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR) · doi:10.1145/3409256.3409817 · Cited by 48
“s that can hold meaningful conversations with users. Towards this direction, researchers have developed conversational systems that support question-answering and task-oriented dialogue, among others [3, 4]. However, it is often the case that in such information-seeking conversations, users fail to express their infor…”
BackgroundGenerating Question-Answer Hierarchies
Krishna, K., & Iyyer, M. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1224 · Cited by 44
“For all of our experiments, we use the AllenNLP implementation of the BiDAF++ question answering model of Choi et al. (2018) trained on QuAC with no dialog context.”
BackgroundMethodInfluentialSelf-citationEvaluating Attribution in Dialogue Systems: The BEGIN Benchmark
Dziri, N., Rashkin, H., Linzen, T., & Reitter, D. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00506 · Cited by 79
“Similar to the conversational QA task (Choi et al., 2018; Reddy et al., 2019), the system is expected to use knowledge to respond to the user query.”
ResultRecent Advances of Foundation Language Models-based Continual Learning: A Survey
Yang, Y., Zhou, J., Ding, X., Huai, T., Liu, S., Chen, Q., Xie, Y., & He, L. (2024) · ACM Computing Surveys · doi:10.1145/3705725 · Cited by 63
“ProgModel [163] have implemented Sinceallexamplescomefromthesametask,thesamemodelcanbeusedtomakepredictionsonallexamples. classification, and AOS [173 Acrucialdifferencebetweenourcontinuallearning Whenanewconceptisintroduced,forwhich f ✓ needstobelearned,agen-erator g 2 G generatesimagesrelatedtothegivenconcept. lever…”
Ohsugi, Y., Saito, I., Nishida, K., Asano, H., & Tomita, J. (2019) · Workshop on NLP for Conversational AI (NLP4ConvAI) · doi:10.18653/v1/w19-4102 · Cited by 55
“HEQ-Q represents the percentage of exceeding the model performance over the human evaluation for each question, and HEQ-D represents the percentage of exceeding the model performance over the human evaluation for each dialogue.”
BackgroundMethodInfluentialWebSRC: A Dataset for Web-Based Structural Reading Comprehension
Chen, X., Zhao, Z., Chen, L., Ji, J., Zhang, D., Luo, A., Xiong, Y., & Yu, K. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.343 · Cited by 97
“Traditional datasets for machine reading comprehension (Talmor et al., 2019; Yang et al., 2018; Ra-jpurkar et al., 2016, 2018; Choi et al., 2018; Reddy et al., 2019; Lai et al., 2017) contain plain text passages and QAs about them.”
BackgroundContext-faithful Prompting for Large Language Models
Zhou, W., Zhang, S., Poon, H., & Chen, M. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.968 · Cited by 141
FlowDelta: Modeling Flow Information Gain in Reasoning for Conversational Machine Comprehension
Yeh, Y. T., & Chen, Y.-N. (2019) · Workshop on Machine Reading for Question Answering (MRQA) · doi:10.18653/v1/d19-5812 · Cited by 50
“Our models are tested on two conversational MC datasets, CoQA (Reddy et al., 2018) and QuAC (Choi et al., 2018), and a sequential instruction understanding dataset, SCONE (Long et al., 2016).”
BackgroundMethodMachine Reading Comprehension: The Role of Contextualized Language Models and BeyondPreprint
Zhang, Z., Zhao, H., & Wang, R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.06249 · Cited by 63
“2018) FF 200K web snippet Q/C Chinese, Baidu Search/Knows QuAC (Choi et al. 2018) FF (+YN) 100K Wikipedia C dialogue-based, 14k dialogs ShARC (Saeidi et al.”
BackgroundMethodInfluentialKnow Your Limits: A Survey of Abstention in Large Language Models
Wen, B., Yao, J., Feng, S., Xu, C., Tsvetkov, Y., Howe, B., & Wang, L. L. (2025) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00754 · Cited by 56
Harnessing Evolution of Multi-Turn Conversations for Effective Answer Retrieval
Aliannejadi, M., Chakraborty, M., Ríssola, E. A., & Crestani, F. (2020) · Conference on Human Information Interaction and Retrieval · doi:10.1145/3343413.3377968 · Cited by 45
“(CAsT)1 in 2019. The track contains 80 conversations on different topics, each of which consists of 8-12 turns (or utterances). Much work has been done on multi-turn conversational question answering [10, 16]. However, not much is known about how users interact with a machine in a multi-turn conversation and how their…”
BackgroundHow Am I Doing?: Evaluating Conversational Search Systems Offline
Lipani, A., Carterette, B., & Yılmaz, E. (2021) · ACM Transactions on Information Systems · doi:10.1145/3451160 · Cited by 45
“Most research on conversational systems has focused on devising user interfaces for conversational systems [4, 11], using knowledge graphs for question answering in a conversational dialogue [23], building neural models for developing conversational systems [42], incorporating context in response generation [13] and a…”
BackgroundContextualized Query Embeddings for Conversational Search
Lin, S.-C., Yang, J.-H., & Lin, J. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.77 · Cited by 55
Automated Question and Answer Generation from Texts using Text-to-Text Transformers
Goyal, R., Kumar, P., & Singh, V. P. (2023) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-023-07840-7 · Cited by 46
“The proposed model has been trained on three datasets, i.e., SQuAD [42], QuAC [43], and BoolQ [44] to generate different types of objective and subjective question-answers.”
ConvGQR: Generative Query Reformulation for Conversational Search
Mo, F., Mao, K., Zhu, Y., Wu, Y., Huang, K., & Nie, J. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.274 · Cited by 79
Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension
Bartolo, M., Roberts, A., Welbl, J., Riedel, S., & Stenetorp, P. (2020) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00338 · Cited by 130
“…sentences or documents (Welbl et al., 2018; Yang et al., 2018), questions requiring rule interpretation or context awareness (Saeidi et al., 2018; Choi et al., 2018; Reddy et al., 2019), limiting annotator passage exposure by sourcing questions first (Kwiatkowski et al., 2019), controlling answer…”
BackgroundSemantic Representation for Dialogue Modeling
Bai, X., Chen, Y., Song, L., & Zhang, Y. (2021) · doi:10.18653/v1/2021.acl-long.342 · Cited by 51
“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.”
BackgroundLi, J., Liu, M., Zheng, Z., Zhang, H., Qin, B., Kan, M., & Liu, T. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533364 · Cited by 42
Proactive Conversational AI: A Comprehensive Survey of Advancements and Opportunities
Deng, Y., Liao, L., Lei, W., Yang, G. H., Lam, W. P., & Chua, T. (2025) · ACM Transactions on Information Systems · doi:10.1145/3715097 · Cited by 67
“For example, Chen et al. [25] propose to prompt LLMs for controllable response generation in emotional support and persuasion dialogues, conditioned on the ground-truth dialogue strategies.”
InfluentialRecurrent Chunking Mechanisms for Long-Text Machine Reading Comprehension
Gong, H., Shen, Y., Yu, D., Chen, J., & Yu, D. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.603 · Cited by 42
Reinforced Dynamic Reasoning for Conversational Question Generation
Pan, B., Li, H., Yao, Z., Cai, D., & Sun, H. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1203 · Cited by 40
“Recently released large-scale datasets (Reddy et al., 2018; Choi et al., 2018) enabled studying it in the textual setting where the information source used to answer questions is a given passage, and they inspired many significant work (Zhu et al., 2018; Huang et al., 2018; Yatskar, 2018).”
BackgroundMethodInfluentialA survey of deep learning techniques for machine reading comprehension
Kazi, S., Khoja, S. A., & Daud, A. Y. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10583-4 · Cited by 44
Peskov, D., Clarke, N., Krone, J., Fodor, B., Zhang, Y., Youssef, A., & Diab, M. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1460 · Cited by 44
“…dialogue dataset collections used for Sequential Question Answering rely on dialogue to answer questions, but the task is notably different from our use case of modeling goal oriented conversational AI, hence leading to different evaluation considerations (Reddy et al., 2019; Choi et al., 2018).”
MethodRobot learning in the era of foundation models: a survey
Xiao, X., Liu, J., Wang, Z., Zhou, Y., Qi, Y., Jiang, S., He, B., & Cheng, Q. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.129963 · Cited by 56
Mačina, J., Daheim, N., Chowdhury, S. P., Sinha, T., Kapur, M., Gurevych, I., & Sachan, M. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.372 · Cited by 62
English Machine Reading Comprehension Datasets: A Survey
Dzendzik, D., Foster, J., & Vogel, C. M. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.693 · Cited by 47
“Those datasets are: AmazonQA, CoQA, DROP, MovieQA, QAngaroo (WikiHop and MedHop), QuAC, ShARC, SQuAD, SQuAD2, TyDi.”
BackgroundInfluentialA dataset and baselines for sequential open-domain question answering
Elgohary, A., Zhao, C., & Boyd-Graber, J. L. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1134 · Cited by 42
“In parallel to our work, Choi et al. (2018) and Reddy et al.”
BackgroundResultOpen-Domain Conversational Agents: Current Progress, Open Problems, and Future Directions
Roller, S., Boureau, Y.-L., Weston, J., Bordes, A., Dinan, E., Fan, A., Gunning, D., Ju, D., Li, M., Poff, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2006.12442 · Cited by 38
“Recent QA datasets have extended to a conversational form with a series of questions possibly referencing earlier ones in the conversation (Choi et al., 2018; Reddy, Chen, and Manning, 2019).”
BackgroundA review of public datasets in question answering research
Cambazoglu, B. B., Sanderson, M., Scholer, F., & Croft, B. W. (2020) · ACM SIGIR Forum · doi:10.1145/3483382.3483389 · Cited by 36
“QuAC , which stands for Question Answering in Context, is a conversational QA dataset [Choi et al., 2018] released in 2018 by researchers from Allen Institute for Artificial Intelligence, University of Washington, Stanford University, and UMass Amherst.”
BackgroundLin, S.-C., Yang, J.-H., Nogueira, R. F., Tsai, M.-F., Wang, C., & Lin, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.01909 · Cited by 85
“With great progress coming from contributions by the NLP and IR communities, high quality datasets for single-turn (Ra-jpurkar et al., 2018; Koˇcisk´y et al., 2018; Dhingra et al., 2017) and multi-turn (conversational) (Reddy et al., 2019; Choi et al., 2018) open-domain QA are available today.”
BackgroundMethodRoy, R. S., & Anand, A. (2021) · Synthesis lectures on information concepts, retrieval, and services · doi:10.2200/s0113ed1v01y202109icr076 · Cited by 37
Tell Me How to Ask Again: Question Data Augmentation with Controllable Rewriting in Continuous Space
Liu, D., Gong, Y., Fu, J., Yan, Y., Chen, J., Lv, J., Duan, N., & Zhou, M. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.467 · Cited by 37
Deep learning-based question answering: a survey
Abdel-Nabi, H., Awajan, A., & Ali, M. Z. (2022) · Knowledge and Information Systems · doi:10.1007/s10115-022-01783-5 · Cited by 44
Automatic Generation of Socratic Subquestions for Teaching Math Word Problems
Shridhar, K., Mačina, J., El‐Assady, M., Sinha, T., Kapur, M., & Sachan, M. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.277 · Cited by 62
Automating Question Generation From Educational Text
Bhowmick, A. K., Jagmohan, A., Vempaty, A., Dey, P., Hall, L., Hartman, J., Kokku, R., & Maheshwari, H. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-47994-6_38 · Cited by 38
Enhancing Conversational Search: Large Language Model-Aided Informative Query Rewriting
Ye, F., Fang, M., Li, S., & Yılmaz, E. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.398 · Cited by 72
Online Distilling from Checkpoints for Neural Machine Translation
Wei, H.-R., Huang, S., Wang, R., Dai, X.-Y., & Chen, J.-J. (2019) · North American Chapter of the Association for Computational Linguistics · doi:10.18653/v1/n19-1192 · Cited by 35
“We apply our ODC method on BiDAF++ (Choi et al., 2018), a multi-layer SQuAD model that augments BiDAF (Seo et al., 2016) with self-attention and contextualized embeddings.”
MethodInfluentialDon't Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset BiasesPreprint
Clark, C., Yatskar, M., & Zettlemoyer, L. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.03683 · Cited by 95
“For example, both CoQA (Reddy et al., 2019) and QuAC (Choi et al., 2018) take steps to prevent annotators from us-ing words that occur in the context passage, VQA 2.0 (Goyal et al., 2018) selects examples to limit the effectiveness of question-only models, and others have filtered examples solvable…”
BackgroundInfluentialSelf-citationDAPIE: Interactive Step-by-Step Explanatory Dialogues to Answer Children’s Why and How Questions
Lee, Y., Kim, T. S., Kim, S., Yun, Y., & Kim, J. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581369 · Cited by 35
“For questioning, we trained a dialogue inpainting model following Dai et al. [16] using four datasets: QuAC [12], QReCC [1], DailyDialog [55], and Taskmaster [6].”
Method“I’d rather just go to bed”: Understanding Indirect Answers
Louis, A. P., Roth, D., & Radlinski, F. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.601 · Cited by 46
InstructDial: Improving Zero and Few-shot Generalization in Dialogue through Instruction Tuning
Gupta, P., Jiao, C., Yeh, Y.-T., Mehri, S., Eskénazi, M., & Bigham, J. P. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.33 · Cited by 75
A proposed conceptual framework for a representational approach to information retrieval
Lin, J. (2021) · ACM SIGIR Forum · doi:10.1145/3527546.3527552 · Cited by 57
“In fact, most participants in the TREC Conversational Assistance Tracks [Dalton et al., 2019], as well as the design of conversational question answering [Choi et al., 2018, Elgohary et al., 2019], adopt this formulation, either implicitly or explicitly.”
BackgroundImproving Question Answering with External KnowledgePreprint
Pan, X., Sun, K., Yu, D., Chen, J., Ji, H., Cardie, C., & Yu, D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.00993 · Cited by 33
“…years have seen numerous datasets (Richardson et al., 2013; Rajpurkar et al., 2016; Lai et al., 2017; Mihaylov et al., 2018; Clark et al., 2018; Choi et al., 2018; Reddy et al., 2018; Sun et al., 2019) and models (Chen et al., 2016; Wang et al., 2018b; Radford et al., 2018; Devlin et al.,…”
BackgroundInfluentialInquisitive Question Generation for High Level Text Comprehension
Ko, W.-J., Chen, T.-Y., Huang, Y., Durrett, G., & Li, J. J. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.530 · Cited by 39
Mavi, V., Jangra, A., & Jatowt, A. (2024) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000102 · Cited by 119
Interactive Language Learning by Question Answering
Yuan, X., Côté, M.-A., Fu, J., Lin, Z., Pal, C., Bengio, Y., & Trischler, A. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1280 · Cited by 36
“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.”
BackgroundAnswering Conversational Questions on Structured Data without Logical Forms
Mueller, T., Piccinno, F., Shaw, P. M., Nicosia, M., & Altün, Y. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1603 · Cited by 43
“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).”
BackgroundNguyen, K. V., Tran, K. V., Luu, S. T., Nguyen, A. G.-T., & Nguyen, N. L.-T. (2020) · IEEE Access · doi:10.1109/access.2020.3035701 · Cited by 32
Kim, G., Kim, H., Park, J., & Kang, J. (2021) · doi:10.18653/v1/2021.acl-long.478 · Cited by 34
“Following Choi et al. (2018), we use the F1, HEQ-Q, and HEQ-D for QuAC and CANARD.”
InfluentialLee, Y.-J., Chung, J. J. Y., Kim, T. S., Song, J. Y., & Kim, J. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3502087 · Cited by 35
“To drive research in this area, researchers have constructed large crowdsourced conversational QA datasets (e.g. CoQA [44], QuAC [13]), which collected dialogues between crowd workers asking and answering a sequence of questions about a source document.”
BackgroundReview on Neural Question Generation for Education Purposes
Faraby, S. A., Adiwijaya, A., & Romadhony, A. (2023) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-023-00374-x · Cited by 44
What Question Answering can Learn from Trivia Nerds
Boyd-Graber, J. L., & Börschinger, B. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.662 · Cited by 31
Improving Skip-Gram Embeddings Using BERT
Wang, Y., Cui, L., & Zhang, Y. (2021) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2021.3065201 · Cited by 32
“Contextualized embeddings are correlated with syntactic [13], semantic [14] and factual knowledge [15], out-performing static embeddings on a wide range of tasks such as question answering [16], reading comprehension [17], commonsense reasoning [18] and natural language inference [19].”
BackgroundDialogue Graph Modeling for Conversational Machine Reading
Ouyang, S., Zhang, Z., & Zhao, H. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.279 · Cited by 33
MMCoQA: Conversational Question Answering over Text, Tables, and Images
Li, Y., Li, W., & Nie, L. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.290 · Cited by 37
“We can see that most of the first words are similar to those questions in other conversational QA datasets (Choi et al., 2018; Reddy et al., 2019; Saha et al., 2018b).”
BackgroundResultA Qualitative Comparison of CoQA, SQuAD 2.0 and QuACPreprint
Yatskar, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.10735 · Cited by 46
“In this work, we analyze three such new proposed datasets, SQuAD 2.0 (Rajpurkar et al., 2018), QuAC (Choi et al., 2018), and CoQA (Reddy et al., 2018).”
MethodInfluentialSelf-citationPrompting Techniques for Secure Code Generation: A Systematic Investigation
Tony, C., Ferreyra, N. E. D., Mutas, M., Dhiff, S., & Scandariato, R. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3722108 · Cited by 85
“…[ 23 ] , W i n o G r a n d e [ 83 ] , P I Q A [ 9 ] , A R C [ 8 ] , U n i fi e d Q A [ 46 ] , O p e n B oo k Q A [ 66 ] , C o Q A [ 80 ] , Q u A C [ 15 ] , D R O P [ 21 ] , S Q u A D [ 79 ] , R A C E [ 52 ] , 2020 N e u r I P S 24786 F e w - s h o t S D L S U P E R G L U E [ 99 ] , R T E [ 11 ] ,…”
MASKER: Masked Keyword Regularization for Reliable Text Classification
Moon, S. J., Mo, S., Lee, K., Lee, J., & Shin, J. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i15.17601 · Cited by 33
Challenges in Information-Seeking QA: Unanswerable Questions and Paragraph Retrieval
Asai, A., & Choi, E. (2021) · doi:10.18653/v1/2021.acl-long.118 · Cited by 44
“However, those state-of-the-art models have difficulty answering information-seeking questions (Kwiatkowski et al., 2019; Choi et al., 2018).”
BackgroundSelf-citationFrom LSAT: The Progress and Challenges of Complex Reasoning
Wang, S., Liu, Z., Zhong, W., Zhou, M., Wei, Z., Chen, Z., & Duan, N. (2022) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2022.3164218 · Cited by 42
MultiSpanQA: A Dataset for Multi-Span Question Answering
Li, H., Tomko, M., Vasardani, M., & Baldwin, T. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.90 · Cited by 33
“0 (Rajpurkar et al., 2018), SearchQA (Dunn et al., 2017), and QuAC (Choi et al., 2018) restrict the answer passage to a single span of text.”
BackgroundDeng, Y., Lei, W., Zhang, W., Lam, W. P., & Chua, T. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.469 · Cited by 53
Conversational Question Answering over Passages by Leveraging Word Proximity Networks
Kaiser, M., Roy, R. S., & Weikum, G. (2020) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3397271.3401399 · Cited by 31
“tandard practiceofdocumentresponses.Thesecrispanswersareaimedatreducing users’ effort in searching for relevant information, and may be in the form of short text passages [5], sentences [19], phrases [3], or entities from a knowledge graph [10]. In this work, we deal with passages: such passage retrieval [17] has long…”
BackgroundEnhanced Speaker-Aware Multi-Party Multi-Turn Dialogue Comprehension
Ma, X., Zhang, Z., & Zhao, H. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3284516 · Cited by 30
“Training models to understand dialogue contexts and answer questions has been shown even more challenging than common machine reading comprehension (MRC) tasks on plain text (Reddy, Chen, and Manning 2019; Choi et al. 2018).”
BackgroundShortcutted Commonsense: Data Spuriousness in Deep Learning of Commonsense Reasoning
Branco, R., Branco, A., Rodrigues, J. A., & Silva, J. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.113 · Cited by 36
“Popular machine reading datasets such as QuAC (Choi et al., 2018), SQuAD 2 (Rajpurkar et al.”
MethodConversational Question Answering on Heterogeneous Sources
Christmann, P., Roy, R. S., & Weikum, G. (2022) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3477495.3531815 · Cited by 29
“Notable efforts at ConvQA benchmarking like QuAC [6] (text), CoQA [37] (text), SQA [14] (tables), ConvQuestions [8] (KB), and CSQA [41] (KB) assume a single answering source.”
BackgroundInfluentialA domain-specific language for describing machine learning datasets
Giner-Miguelez, J., Gómez, A., & Cabot, J. (2023) · Journal of Computer Languages · doi:10.1016/j.cola.2023.101209 · Cited by 29
R-Trans: RNN Transformer Network for Chinese Machine Reading Comprehension
Liu, S., Zhang, S., Zhang, X., & Wang, H. (2019) · IEEE Access · doi:10.1109/access.2019.2901547 · Cited by 30
“‘‘QuAC: Question answering in context.’’”
BackgroundLi, K., Xian, X., Wang, J., & Yu, N. (2018) · Applied Surface Science · doi:10.1016/j.apsusc.2018.11.214 · Cited by 30
“werer gives answers, which simulates a conversation between two humans when reading the passage. There is no limit to answer form on CoQA which requires more context reasoning. Similarly, Choi et al. [12] introduce QuAC for question answering in context. Compared to the CoQA, passages are only given to the answerer, w…”
BackgroundTowards Data Distillation for End-to-end Spoken Conversational Question AnsweringPreprint
You, C., Chen, N., Liu, F., Yang, D., & Zou, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.08923 · Cited by 41
“et al., 2020; Gong et al., 2020). Within the growing body of work on conversational machine reading comprehension, two signature attributes have emerged: the availability of large benchmark datasets (Choi et al., 2018; Elgohary et al., 2018; Reddy et al., 2019) and pre-trained language models (Devlin et al., 2018; Liu…”
BackgroundMethodInfluentialGeneral Greedy De-Bias Learning
Han, X., Wang, S., Su, C., Huang, Q., & Tian, Q. C. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3240337 · Cited by 29
“[41] E. Choi et al., “QuAC: Question answering in context,” in Proc.”
MethodGenerative retrieval for conversational question answering
Li, Y., Yang, N., Wang, L., Wei, F., & Li, W. (2023) · Information Processing & Management · doi:10.1016/j.ipm.2023.103475 · Cited by 34
“OR-QuAC (Qu et al., 2020) is transformed from the conversational MRC dataset QuAC (Choi et al., 2018).”
InfluentialOn Making Reading Comprehension More Comprehensive
Gardner, M., Berant, J., Hajishirzi, H., Talmor, A., & Min, S. (2019) · Workshop on Machine Reading for Question Answering (MRQA) · doi:10.18653/v1/d19-5815 · Cited by 32
“, 2016)4, QUAC (Choi et al., 2018) and NATURAL QUESTIONS (Kwiatkowski et al.”
BackgroundMethodInfluentialSynthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering
Riabi, A., Scialom, T., Keraron, R., Sagot, B., Seddah, D., & Staiano, J. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.562 · Cited by 44
“Given that the majority of large scale Question Answering (QA) datasets are in English (Hermann et al., 2015; Rajpurkar et al., 2016; Choi et al., 2018), the development of QA systems targeting other languages is currently addressed via two cross-lingual QA datasets: XQuAD (Artetxe et al., 2020)…”
BackgroundDynamic Graph Reasoning for Conversational Open-Domain Question Answering
Li, Y., Li, W., & Nie, L. (2022) · ACM Transactions on Information Systems · doi:10.1145/3498557 · Cited by 29
“Conversational KBQA aims to answer conversational questions with the relevant facts in a knowledge base, whereas conversational MRC answers a series of questions over a given passage in a dialog.”
BackgroundMethodResultInfluentialChainCQG: Flow-Aware Conversational Question Generation
Gu, J., Mirshekari, M., Yu, Z., & Sisto, A. (2021) · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume · doi:10.18653/v1/2021.eacl-main.177 · Cited by 28
Structural Characterization for Dialogue Disentanglement
Ma, X., Zhang, Z., & Zhao, H. Y. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.23 · Cited by 29
“…anytime and anywhere, especially as the booming social network services hugely facilitate open conversations, such as group chatting and forum discussion, producing various tangled dialogue logs (Lowe et al., 2015; Zhang et al., 2018b; Choi et al., 2018; Reddy et al., 2019; Li et al., 2020a).”
BackgroundMaking Information Seeking Easier: An Improved Pipeline for Conversational Search
Kumar, V., & Callan, J. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.354 · Cited by 29
“Prior to the CAsT dataset, researchers often utilised dialog response reranking tasks (Zhou et al., 2016; Wu et al., 2016), conversational question-answering (Choi et al., 2018; Reddy et al., 2019) and voice based recommendation (Zhang et al., 2018) as a ‘proxy’ for a conversational search setting.”
MethodUsing Large Language Models to Generate, Validate, and Apply User Intent Taxonomies
Shah, C., White, R., Andersen, R., Buscher, G., Counts, S., Das, S., Montazer, A., Manivannan, S., Neville, J., Rangan, N., et al. (2025) · ACM Transactions on the Web · doi:10.1145/3732294 · Cited by 33
Transformers to Learn Hierarchical Contexts in Multiparty Dialogue for Span-based Question Answering
Li, C., & Choi, J. D. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.505 · Cited by 25
Conversational Semantic Role Labeling
Xu, K., Wu, H., Song, L., Zhang, H., Song, L., & Yu, D. (2021) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2021.3074014 · Cited by 25
Ren, P., Liu, Z., Song, X., Tian, H., Chen, Z., Ren, Z., & de Rijke, M. (2021) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3404835.3462897 · Cited by 27
“, knowledge grounded conversations [14, 22], conversational question answering [8, 10, 29, 32].”
BackgroundQAConv: Question Answering on Informative Conversations
Wu, C.-S., Madotto, A., Liu, W., Fung, P., & Xiong, C. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.370 · Cited by 32
Chen, S., Jin, S., & Xie, X. (2021) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3468264.3468569 · Cited by 28
“Boolean questions make up an important subset in many popular MRC benchmark datasets [9, 24, 34].”
BackgroundGraph Neural Networks for Natural Language Processing: A Survey
Wu, L., Chen, Y., Shen, K., Guo, X., Gao, H., Li, S., Pei, J., & Long, B. (2023) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000096 · Cited by 417
Question Rewriting for Conversational Question Answering
Vakulenko, S., Longpre, S., Tu, Z., & Anantha, R. (2021) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3437963.3441748 · Cited by 42
“implementation. 4.1 Datasets We chose two conversational QA datasets for the evaluation of our approach: (1) CANARD, derived from Question Answering in Context (QuAC) for extractive conversational QA [2], and (2) TREC CAsT for retrieval conversational QA [5]. Following the setup of the TREC CAsT 2019, we use the MS MA…”
MethodInfluentialMLSUM: The Multilingual Summarization Corpus
Scialom, T., Dray, P.-A., Lamprier, S., Piwowarski, B., & Staiano, J. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.647 · Cited by 64
What’s The Latest? A Question-driven News Chatbot
Laban, P., Canny, J., & Hearst, M. A. (2020) · Annual Meeting of the Association for Computational Linguistics: System Demonstrations (ACL Demo) · doi:10.18653/v1/2020.acl-demos.43 · Cited by 27
“CoQA (Reddy et al., 2019) and Quac (Choi et al., 2018) are two datasets collected for questions answering in the context of a dialogue.”
InfluentialLi, R., Jiang, Z., Wang, L., Lu, X., Zhao, M., & Chen, D. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.106936 · Cited by 28
Reinforced History Backtracking for Conversational Question Answering
Qiu, M., Huang, X., Chen, C., Ji, F., Qu, C., Wei, W., Huang, J., & Zhang⋆, Y. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i15.17617 · Cited by 25
“Conv ersational Q uestion A nswering (ConvQA) have recently interested a lot of researchers in both research community and industry (Choi et al. 2018b; Reddy, Chen, and Manning 2018a).”
BackgroundEvaluating Human-AI Hybrid Conversational Systems with Chatbot Message Suggestions
Gao, Z., & Jiang, J. (2021) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3459637.3482340 · Cited by 30
“On the one hand, many existing datasets are not using natural online conversations, but online social media discourses or movie lines [5, 7, 10, 25, 33, 37, 45].”
BackgroundReason first, then respond: Modular Generation for Knowledge-infused Dialogue
Adolphs, L., Shuster, K., Urbanek, J., Szlam, A., & Weston, J. (2022) · Findings of the Association for Computational Linguistics: EMNLP 2022 · doi:10.18653/v1/2022.findings-emnlp.527 · Cited by 25
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 ›
Are you an author, or do you see a problem with this page? Request a review
