TransferNet: An Effective and Transparent Framework for Multi-hop Question Answering over Relation Graph

Jiaxin Shi, Shulin Cao, Lei Hou, Juanzi Li, Hanwang Zhang


Abstract
Multi-hop Question Answering (QA) is a challenging task because it requires precise reasoning with entity relations at every step towards the answer. The relations can be represented in terms of labels in knowledge graph (e.g., spouse) or text in text corpus (e.g., they have been married for 26 years). Existing models usually infer the answer by predicting the sequential relation path or aggregating the hidden graph features. The former is hard to optimize, and the latter lacks interpretability. In this paper, we propose TransferNet, an effective and transparent model for multi-hop QA, which supports both label and text relations in a unified framework. TransferNet jumps across entities at multiple steps. At each step, it attends to different parts of the question, computes activated scores for relations, and then transfer the previous entity scores along activated relations in a differentiable way. We carry out extensive experiments on three datasets and demonstrate that TransferNet surpasses the state-of-the-art models by a large margin. In particular, on MetaQA, it achieves 100% accuracy in 2-hop and 3-hop questions. By qualitative analysis, we show that TransferNet has transparent and interpretable intermediate results.
Anthology ID:
2021.emnlp-main.341
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Editors:
Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4149–4158
Language:
URL:
https://aclanthology.org/2021.emnlp-main.341
DOI:
10.18653/v1/2021.emnlp-main.341
Bibkey:
Cite (ACL):
Jiaxin Shi, Shulin Cao, Lei Hou, Juanzi Li, and Hanwang Zhang. 2021. TransferNet: An Effective and Transparent Framework for Multi-hop Question Answering over Relation Graph. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 4149–4158, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
TransferNet: An Effective and Transparent Framework for Multi-hop Question Answering over Relation Graph (Shi et al., EMNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.emnlp-main.341.pdf
Video:
 https://aclanthology.org/2021.emnlp-main.341.mp4
Code
 shijx12/TransferNet
Data
MetaQASimpleQuestionsWikiMovies