@inproceedings{gupta-etal-2020-reinforced,
title = "Reinforced Multi-task Approach for Multi-hop Question Generation",
author = "Gupta, Deepak and
Chauhan, Hardik and
Akella, Ravi Tej and
Ekbal, Asif and
Bhattacharyya, Pushpak",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.249/",
doi = "10.18653/v1/2020.coling-main.249",
pages = "2760--2775",
abstract = "Question generation (QG) attempts to solve the inverse of question answering (QA) problem by generating a natural language question given a document and an answer. While sequence to sequence neural models surpass rule-based systems for QG, they are limited in their capacity to focus on more than one supporting fact. For QG, we often require multiple supporting facts to generate high-quality questions. Inspired by recent works on multi-hop reasoning in QA, we take up Multi-hop question generation, which aims at generating relevant questions based on supporting facts in the context. We employ multitask learning with the auxiliary task of answer-aware supporting fact prediction to guide the question generator. In addition, we also proposed a question-aware reward function in a Reinforcement Learning (RL) framework to maximize the utilization of the supporting facts. We demonstrate the effectiveness of our approach through experiments on the multi-hop question answering dataset, HotPotQA. Empirical evaluation shows our model to outperform the single-hop neural question generation models on both automatic evaluation metrics such as BLEU, METEOR, and ROUGE and human evaluation metrics for quality and coverage of the generated questions."
}
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<abstract>Question generation (QG) attempts to solve the inverse of question answering (QA) problem by generating a natural language question given a document and an answer. While sequence to sequence neural models surpass rule-based systems for QG, they are limited in their capacity to focus on more than one supporting fact. For QG, we often require multiple supporting facts to generate high-quality questions. Inspired by recent works on multi-hop reasoning in QA, we take up Multi-hop question generation, which aims at generating relevant questions based on supporting facts in the context. We employ multitask learning with the auxiliary task of answer-aware supporting fact prediction to guide the question generator. In addition, we also proposed a question-aware reward function in a Reinforcement Learning (RL) framework to maximize the utilization of the supporting facts. We demonstrate the effectiveness of our approach through experiments on the multi-hop question answering dataset, HotPotQA. Empirical evaluation shows our model to outperform the single-hop neural question generation models on both automatic evaluation metrics such as BLEU, METEOR, and ROUGE and human evaluation metrics for quality and coverage of the generated questions.</abstract>
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%0 Conference Proceedings
%T Reinforced Multi-task Approach for Multi-hop Question Generation
%A Gupta, Deepak
%A Chauhan, Hardik
%A Akella, Ravi Tej
%A Ekbal, Asif
%A Bhattacharyya, Pushpak
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F gupta-etal-2020-reinforced
%X Question generation (QG) attempts to solve the inverse of question answering (QA) problem by generating a natural language question given a document and an answer. While sequence to sequence neural models surpass rule-based systems for QG, they are limited in their capacity to focus on more than one supporting fact. For QG, we often require multiple supporting facts to generate high-quality questions. Inspired by recent works on multi-hop reasoning in QA, we take up Multi-hop question generation, which aims at generating relevant questions based on supporting facts in the context. We employ multitask learning with the auxiliary task of answer-aware supporting fact prediction to guide the question generator. In addition, we also proposed a question-aware reward function in a Reinforcement Learning (RL) framework to maximize the utilization of the supporting facts. We demonstrate the effectiveness of our approach through experiments on the multi-hop question answering dataset, HotPotQA. Empirical evaluation shows our model to outperform the single-hop neural question generation models on both automatic evaluation metrics such as BLEU, METEOR, and ROUGE and human evaluation metrics for quality and coverage of the generated questions.
%R 10.18653/v1/2020.coling-main.249
%U https://aclanthology.org/2020.coling-main.249/
%U https://doi.org/10.18653/v1/2020.coling-main.249
%P 2760-2775
Markdown (Informal)
[Reinforced Multi-task Approach for Multi-hop Question Generation](https://aclanthology.org/2020.coling-main.249/) (Gupta et al., COLING 2020)
ACL
- Deepak Gupta, Hardik Chauhan, Ravi Tej Akella, Asif Ekbal, and Pushpak Bhattacharyya. 2020. Reinforced Multi-task Approach for Multi-hop Question Generation. In Proceedings of the 28th International Conference on Computational Linguistics, pages 2760–2775, Barcelona, Spain (Online). International Committee on Computational Linguistics.