Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering

Yanlin Feng, Xinyue Chen, Bill Yuchen Lin, Peifeng Wang, Jun Yan, Xiang Ren


Abstract
Existing work on augmenting question answering (QA) models with external knowledge (e.g., knowledge graphs) either struggle to model multi-hop relations efficiently, or lack transparency into the model’s prediction rationale. In this paper, we propose a novel knowledge-aware approach that equips pre-trained language models (PTLMs) has with a multi-hop relational reasoning module, named multi-hop graph relation network (MHGRN). It performs multi-hop, multi-relational reasoning over subgraphs extracted from external knowledge graphs. The proposed reasoning module unifies path-based reasoning methods and graph neural networks to achieve better interpretability and scalability. We also empirically show its effectiveness and scalability on CommonsenseQA and OpenbookQA datasets, and interpret its behaviors with case studies, with the code for experiments released.
Anthology ID:
2020.emnlp-main.99
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1295–1309
Language:
URL:
https://aclanthology.org/2020.emnlp-main.99
DOI:
10.18653/v1/2020.emnlp-main.99
Bibkey:
Cite (ACL):
Yanlin Feng, Xinyue Chen, Bill Yuchen Lin, Peifeng Wang, Jun Yan, and Xiang Ren. 2020. Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1295–1309, Online. Association for Computational Linguistics.
Cite (Informal):
Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering (Feng et al., EMNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.99.pdf
Optional supplementary material:
 2020.emnlp-main.99.OptionalSupplementaryMaterial.zip
Video:
 https://slideslive.com/38938959
Code
 INK-USC/MHGRN +  additional community code
Data
CommonsenseQAConceptNetOpenBookQA