Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus
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Iulian Vlad Serban, Alberto García-Durán, Caglar Gulcehre, Sungjin Ahn, Sarath Chandar, Aaron Courville, Yoshua Bengio. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2016.
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Two main approaches exist for open-domain questions: one uses syntactic structure and NLP operations, the other uses an end-to-end neural machine translation-like method.
“There are two major approaches for open-domain questions, which have been researched upon. The first approach makes use of the syntactic structure of sentences and other natural language processing operations to produce a question from a specified sentence, and the second approach makes use of an end-to-end method which uses an approach similar to machine language translation using neural networks”
From Automatic question generation: a review of methodologies, datasets, evaluation metrics, and applications (2023) · cites this paper as [11]
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References 25 · Cited by 280
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- Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural machine translation by jointly learning to align and translate. In International Conference on Learning Represen- tations.
- Wei Chen, Gregory Aist, and Jack Mostow. 2009. Generating questions automati- cally from informational text. In Proceedings of the 2nd Workshop on Question Generation (AIED 2009), pages 17-24.
- Cho et al.2014] Kyunghyun Cho, Bart van Merrien- boer, Caglar Gulcehre, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 1724-1734.
- Kalady et al.2010] Saidalavi Kalady, Ajeesh Elikkottil, and Rajarshi Das. 2010. Natural language question generation using syntax and keywords. In Proceed- ings of QG2010: The Third Workshop on Question Generation, pages 1-10. questiongeneration. org.
- and Guha1989] Douglas B. Lenat and Ra- manathan V. Guha. 1989. Building large knowledge-based systems; representation and infer- ence in the Cyc project. Addison-Wesley Longman Publishing Co., Inc.
- Vanessa Lopez, Victoria Uren, Marta Sabou, and Enrico Motta. 2011. Is question answering fit for the semantic web? a survey. Semantic Web, 2(2):125-155.
- Vasile Rus, Brendan Wyse, Paul Pi- wek, Mihai Lintean, Svetlana Stoyanchev, and Cris- tian Moldovan. 2010. The first question generation shared task evaluation challenge. In Proceedings of the 6th International Natural Language Generation Conference, pages 251-257.
- Development Team2016] Theano Develop- ment Team. 2016. Theano: A Python framework for fast computation of mathematical expressions. arXiv e-prints, abs/1605.02688, May. · arXiv
- Vrandečić and Krötzsch2014] Denny Vrandečić and Markus Krötzsch. 2014. Wikidata: a free collabora- tive knowledgebase. Communications of the ACM, 57(10):78-85.
- Xuchen Yao, Gosse Bouma, and Yi Zhang. 2012. Semantics-based question genera- tion and implementation. Dialogue and Discourse, 3(2):11-42.
- Authors
- Serban, I. V., García-Durán, A., Gulcehre, C., Ahn, S., Chandar, S., Courville, A., & Bengio, Y.
- Affiliations
- Université de Montréal (CA)
- Université de Technologie de Compiègne (FR)
- Canadian Institute for Advanced Research (CA)
- Venue
- Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2016
- Pages
- 588-598
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W2304545146
- Cited by
- 280
- References
- 25
- Citations by year
- 2026: 2 · 2025: 10 · 2024: 20 · 2023: 21 · 2022: 26 · 2021: 33 · 2020: 36 · 2019: 59 · 2018: 42 · 2017: 23 · 2016: 8
- FWCI
- 23.71
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- PolyPublie (École Polytechnique de Montréal) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Canadian Institute for Advanced Research
- Compute Canada
- Canada Research Chairs
- Natural Sciences and Engineering Research Council of Canada
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- question answering, recurrent neural networks, question generation
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