S3HQA: A Three-Stage Approach for Multi-hop Text-Table Hybrid Question Answering

Fangyu Lei, Xiang Li, Yifan Wei, Shizhu He, Yiming Huang, Jun Zhao, Kang Liu


Abstract
Answering multi-hop questions over hybrid factual knowledge from the given text and table (TextTableQA) is a challenging task. Existing models mainly adopt a retriever-reader framework, which have several deficiencies, such as noisy labeling in training retriever, insufficient utilization of heterogeneous information over text and table, and deficient ability for different reasoning operations. In this paper, we propose a three-stage TextTableQA framework S3HQA, which comprises of retriever, selector, and reasoner. We use a retriever with refinement training to solve the noisy labeling problem. Then, a hybrid selector considers the linked relationships between heterogeneous data to select the most relevant factual knowledge. For the final stage, instead of adapting a reading comprehension module like in previous methods, we employ a generation-based reasoner to obtain answers. This includes two approaches: a row-wise generator and an LLM prompting generator (first time used in this task). The experimental results demonstrate that our method achieves competitive results in the few-shot setting. When trained on the full dataset, our approach outperforms all baseline methods, ranking first on the HybridQA leaderboard.
Anthology ID:
2023.acl-short.147
Volume:
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1731–1740
Language:
URL:
https://aclanthology.org/2023.acl-short.147
DOI:
10.18653/v1/2023.acl-short.147
Bibkey:
Cite (ACL):
Fangyu Lei, Xiang Li, Yifan Wei, Shizhu He, Yiming Huang, Jun Zhao, and Kang Liu. 2023. S3HQA: A Three-Stage Approach for Multi-hop Text-Table Hybrid Question Answering. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 1731–1740, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
S3HQA: A Three-Stage Approach for Multi-hop Text-Table Hybrid Question Answering (Lei et al., ACL 2023)
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PDF:
https://aclanthology.org/2023.acl-short.147.pdf
Video:
 https://aclanthology.org/2023.acl-short.147.mp4