2016 GeneratingFactoidQuestionsWithR

• (Serban et al., 2016) ⇒ Iulian Vlad Serban, Alberto Garcia-Duran, Caglar Gulcehre, Sungjin Ahn, Sarath Chandar, Aaron C. Courville, and Yoshua Bengio. (2016). “Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus.” In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL 2016), Volume 1: Long Papers. DOI:10.18653/v1/P16-1056.

Subject Headings: Natural Language Generation Task; Factoid Question Generation Task.

Notes

• Pre-Print(s) and Other Link(s):
  • ACL Anthology: https://www.aclweb.org/anthology/P16-1056/
  • ArXiv: https://arxiv.org/abs/1603.06807
  • DBLP: https://dblp.org/rec/html/conf/acl/SerbanGGACCB16

Cited By

• Google Scholar: ~ 145 Citations.
• Semantic Scholar: ~ 154 Citations.
• MS Academic: ~ 123 Citations.

Quotes

Abstract

Over the past decade, large-scale supervised learning corpora have enabled machine learning researchers to make substantial advances. However, to this date, there are no large-scale questionanswer corpora available. In this paper we present the 30M Factoid Question-Answer Corpus, an enormous question-answer pair corpus produced by applying a novel neural network architecture on the knowledge base Freebase to transduce facts into natural language questions. The produced question-answer pairs are evaluated both by human evaluators and using automatic evaluation metrics, including well-established machine translation and sentence similarity metrics. Across all evaluation criteria the questiongeneration model outperforms the competing template-based baseline. Furthermore, when presented to human evaluators, the generated questions appear to be comparable in quality to real human-generated questions.

BibTeX

@inproceedings{2016_GeneratingFactoidQuestionsWithR,
  author    = {Iulian Vlad Serban and
               Alberto Garcia-Duran and
               Caglar Gulcehre and
               Sungjin Ahn and
               Sarath Chandar and
               [[Aaron C. Courville]] and
               [[Yoshua Bengio]]},
  title     = {Generating Factoid Questions With Recurrent Neural Networks: The 30M
               Factoid Question-Answer Corpus},
  booktitle = {Proceedings of the 54th Annual Meeting of the Association for Computational
               Linguistics (ACL 2016), August 7-12, 2016, Berlin, Germany, Volume
               1: Long Papers},
  publisher = {The Association for Computer Linguistics},
  year      = {2016},
  url       = {https://doi.org/10.18653/v1/p16-1056},
  doi       = {10.18653/v1/p16-1056},
}

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2016 GeneratingFactoidQuestionsWithR	Yoshua Bengio Sungjin Ahn Caglar Gulcehre Sarath Chandar Aaron C. Courville Iulian Vlad Serban Alberto Garcia-Duran			Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus						2016