Reinforced Multi-task Approach for Multi-hop Question Generation
Deepak Gupta, Hardik Chauhan, Ravi Tej Akella, Asif Ekbal, Pushpak Bhattacharyya
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.- Anthology ID:
- 2020.coling-main.249
- Volume:
- Proceedings of the 28th International Conference on Computational Linguistics
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona, Spain (Online)
- Editors:
- Donia Scott, Nuria Bel, Chengqing Zong
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 2760–2775
- Language:
- URL:
- https://aclanthology.org/2020.coling-main.249
- DOI:
- 10.18653/v1/2020.coling-main.249
- Cite (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.
- Cite (Informal):
- Reinforced Multi-task Approach for Multi-hop Question Generation (Gupta et al., COLING 2020)
- PDF:
- https://preview.aclanthology.org/landing_page/2020.coling-main.249.pdf
- Data
- HotpotQA, SQuAD