@inproceedings{qiu-xiong-2019-generating,
title = "Generating Highly Relevant Questions",
author = "Qiu, Jiazuo and
Xiong, Deyi",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1614",
doi = "10.18653/v1/D19-1614",
pages = "5983--5987",
abstract = "The neural seq2seq based question generation (QG) is prone to generating generic and undiversified questions that are poorly relevant to the given passage and target answer. In this paper, we propose two methods to address the issue. (1) By a partial copy mechanism, we prioritize words that are morphologically close to words in the input passage when generating questions; (2) By a QA-based reranker, from the n-best list of question candidates, we select questions that are preferred by both the QA and QG model. Experiments and analyses demonstrate that the proposed two methods substantially improve the relevance of generated questions to passages and answers.",
}
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%0 Conference Proceedings
%T Generating Highly Relevant Questions
%A Qiu, Jiazuo
%A Xiong, Deyi
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F qiu-xiong-2019-generating
%X The neural seq2seq based question generation (QG) is prone to generating generic and undiversified questions that are poorly relevant to the given passage and target answer. In this paper, we propose two methods to address the issue. (1) By a partial copy mechanism, we prioritize words that are morphologically close to words in the input passage when generating questions; (2) By a QA-based reranker, from the n-best list of question candidates, we select questions that are preferred by both the QA and QG model. Experiments and analyses demonstrate that the proposed two methods substantially improve the relevance of generated questions to passages and answers.
%R 10.18653/v1/D19-1614
%U https://aclanthology.org/D19-1614
%U https://doi.org/10.18653/v1/D19-1614
%P 5983-5987
Markdown (Informal)
[Generating Highly Relevant Questions](https://aclanthology.org/D19-1614) (Qiu & Xiong, EMNLP-IJCNLP 2019)
ACL
- Jiazuo Qiu and Deyi Xiong. 2019. Generating Highly Relevant Questions. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5983–5987, Hong Kong, China. Association for Computational Linguistics.