A Sequential Flow Control Framework for Multi-hop Knowledge Base Question Answering

Minghui Xie, Chuzhan Hao, Peng Zhang


Abstract
One of the key challenges of knowledge base question answering (KBQA) is the multi-hop reasoning. Since in different hops, one attends to different parts of question, it is important to dynamically represent the question semantics for each hop. Existing methods, however, (i) infer the dynamic question representation only through coarse-grained attention mechanisms, which may bring information loss, (ii) and have not effectively modeled the sequential logic, which is crucial for the multi-hop reasoning process in KBQA.To address these issues, we propose a sequential reasoning self-attention mechanism to capture the crucial reasoning information of each single hop in a more fine-grained way. Based on Gated Recurrent Unit (GRU) which is good at modeling sequential process, we propose a simple but effective GRU-inspired Flow Control (GFC) framework to model sequential logic in the whole multi-hop process.Extensive experiments on three popular benchmark datasets have demonstrated the superior effectiveness of our model. In particular, GFC achieves new state-of-the-art Hits@1 of 76.8% on WebQSP and is also effective when KB is incomplete. Our code and data are available at https://github.com/Xie-Minghui/GFC.
Anthology ID:
2022.emnlp-main.578
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
8450–8460
Language:
URL:
https://aclanthology.org/2022.emnlp-main.578
DOI:
10.18653/v1/2022.emnlp-main.578
Bibkey:
Cite (ACL):
Minghui Xie, Chuzhan Hao, and Peng Zhang. 2022. A Sequential Flow Control Framework for Multi-hop Knowledge Base Question Answering. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 8450–8460, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
A Sequential Flow Control Framework for Multi-hop Knowledge Base Question Answering (Xie et al., EMNLP 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.emnlp-main.578.pdf
Dataset:
 2022.emnlp-main.578.dataset.zip
Software:
 2022.emnlp-main.578.software.zip