Towards Answer-unaware Conversational Question Generation
Open accessAbstract
Conversational question generation is a novel area of NLP research which has a range of potential applications. This paper is first to presents a framework for conversational question generation that is unaware of the corresponding answers. To properly generate a question coherent to the grounding text and the current conversation history, the proposed framework first locates the focus of a question in the text passage, and then identifies the question pattern that leads the sequential generation of the words in a question. The experiments using the CoQA dataset demonstrate that the quality of generated questions greatly improves if the question foci and the question patterns are correctly identified. In addition, it was shown that the question foci, even estimated with a reasonable accuracy, could contribute to the quality improvement. These results established that our research direction may be promising, but at the same time revealed that the identification of question patterns is a challenging issue, and it has to be largely refined to achieve a better quality in the end-to-end automatic question generation.
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Cites
Glove: Global Vectors for Word Representation
Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071 · see citing works
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 · see citing works
CoQA: A Conversational Question Answering Challenge
Reddy, S., Chen, D., & Manning, C. D. (2019) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00266 · Cited by 975 · see citing works
QuAC: Question Answering in Context
Choi, E., He, H., Iyyer, M., Yatskar, M., Yih, W.-T., Choi, Y., Liang, P., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1241 · Cited by 714 · cited 2× · see citing works
“Datasets such as CoQA (Reddy et al., 2019) and QuAC (Choi et al., 2018) have been developed to facilitate the relevant research efforts (Yatskar, 2019).”
BackgroundLearning to Ask: Neural Question Generation for Reading Comprehension
Du, X., Shao, J., & Cardie, C. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1123 · Cited by 696 · cited 3× · see citing works
“Succeeding the pioneering work (Du et al., 2017), several proposals (Zhou et al., 2017; Du and Cardie, 2018; Yuan et al., 2017; Tang et al., 2017) have been made to chiefly improve the quality of generated questions.”
BackgroundMethodNeural Question Generation from Text: A Preliminary Study
Zhou, Q. S., Yang, N., Wei, F., Tan, C., Bao, H., & Zhou, M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-73618-1_56 · Cited by 353 · see citing works
Harvesting Paragraph-level Question-Answer Pairs from Wikipedia
Du, X., & Cardie, C. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1177 · Cited by 168 · see citing works
Machine Comprehension by Text-to-Text Neural Question Generation
Yuan, X., Wang, T., Gülçehre, Ç., Sordoni, A., Bachman, P., Zhang, S., Subramanian, S., & Trischler, A. (2017) · Workshop on Representation Learning for NLP (RepL4NLP) · doi:10.18653/v1/w17-2603 · Cited by 158 · see citing works
Question Answering and Question Generation as Dual TasksPreprint
Tang, D., Duan, N., Qin, T., Yan, Z., & Zhou, M. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.02027 · Cited by 155 · see citing works
Recent Advances in Neural Question GenerationPreprint
Pan, L., Lei, W., Chua, T., & Kan, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.08949 · Cited by 79 · see citing works
A Qualitative Comparison of CoQA, SQuAD 2.0 and QuAC
Yatskar, M. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1241 · Cited by 56 · see citing works
Interconnected Question Generation with Coreference Alignment and Conversation Flow Modeling
Gao, Y., Li, P., King, I., & Lyu, M. R. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1480 · Cited by 44 · see citing works
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Mulla, N., & Gharpure, P. (2023) · Progress in Artificial Intelligence · doi:10.1007/s13748-023-00295-9 · Cited by 132 · see citing works
ChainCQG: 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 · see citing works
Qi, P., Zhang, Y., & Manning, C. D. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.3 · Cited by 23 · see citing works
- Authors
- Nakanishi, M., Kobayashi, T., & Hayashi, Y.
- ORCID
- Hayashi, Yoshihiko
- Venue
- Proceedings of the 2nd Workshop on Machine Reading for Question Answering
- Published
- 2019
- OpenAlex
- W2988266977
- Cited by
- 21
- References
- 12
- Citations by year
- 2024: 1 · 2023: 7 · 2022: 5 · 2021: 5 · 2020: 3
- FWCI
- 1.24
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Workshop on Machine Reading for Question Answering (MRQA) (publishedVersion, cc-by)
- Funders
- Japan Society for the Promotion of Science (18H04128)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- conversation history
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