Filtering before Iteratively Referring for Knowledge-Grounded Response Selection in Retrieval-Based Chatbots

Jia-Chen Gu, Zhenhua Ling, Quan Liu, Zhigang Chen, Xiaodan Zhu


Abstract
The challenges of building knowledge-grounded retrieval-based chatbots lie in how to ground a conversation on its background knowledge and how to match response candidates with both context and knowledge simultaneously. This paper proposes a method named Filtering before Iteratively REferring (FIRE) for this task. In this method, a context filter and a knowledge filter are first built, which derive knowledge-aware context representations and context-aware knowledge representations respectively by global and bidirectional attention. Besides, the entries irrelevant to the conversation are discarded by the knowledge filter. After that, iteratively referring is performed between context and response representations as well as between knowledge and response representations, in order to collect deep matching features for scoring response candidates. Experimental results show that FIRE outperforms previous methods by margins larger than 2.8% and 4.1% on the PERSONA-CHAT dataset with original and revised personas respectively, and margins larger than 3.1% on the CMU_DoG dataset in terms of top-1 accuracy. We also show that FIRE is more interpretable by visualizing the knowledge grounding process.
Anthology ID:
2020.findings-emnlp.127
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1412–1422
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.127
DOI:
10.18653/v1/2020.findings-emnlp.127
Bibkey:
Cite (ACL):
Jia-Chen Gu, Zhenhua Ling, Quan Liu, Zhigang Chen, and Xiaodan Zhu. 2020. Filtering before Iteratively Referring for Knowledge-Grounded Response Selection in Retrieval-Based Chatbots. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1412–1422, Online. Association for Computational Linguistics.
Cite (Informal):
Filtering before Iteratively Referring for Knowledge-Grounded Response Selection in Retrieval-Based Chatbots (Gu et al., Findings 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.findings-emnlp.127.pdf
Video:
 https://slideslive.com/38940705
Code
 JasonForJoy/FIRE
Data
CMU DoG