An Interpretable Neuro-Symbolic Reasoning Framework for Task-Oriented Dialogue Generation

Shiquan Yang, Rui Zhang, Sarah Erfani, Jey Han Lau


Abstract
We study the interpretability issue of task-oriented dialogue systems in this paper. Previously, most neural-based task-oriented dialogue systems employ an implicit reasoning strategy that makes the model predictions uninterpretable to humans. To obtain a transparent reasoning process, we introduce neuro-symbolic to perform explicit reasoning that justifies model decisions by reasoning chains. Since deriving reasoning chains requires multi-hop reasoning for task-oriented dialogues, existing neuro-symbolic approaches would induce error propagation due to the one-phase design. To overcome this, we propose a two-phase approach that consists of a hypothesis generator and a reasoner. We first obtain multiple hypotheses, i.e., potential operations to perform the desired task, through the hypothesis generator. Each hypothesis is then verified by the reasoner, and the valid one is selected to conduct the final prediction. The whole system is trained by exploiting raw textual dialogues without using any reasoning chain annotations. Experimental studies on two public benchmark datasets demonstrate that the proposed approach not only achieves better results, but also introduces an interpretable decision process.
Anthology ID:
2022.acl-long.338
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4918–4935
Language:
URL:
https://aclanthology.org/2022.acl-long.338
DOI:
10.18653/v1/2022.acl-long.338
Bibkey:
Cite (ACL):
Shiquan Yang, Rui Zhang, Sarah Erfani, and Jey Han Lau. 2022. An Interpretable Neuro-Symbolic Reasoning Framework for Task-Oriented Dialogue Generation. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 4918–4935, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
An Interpretable Neuro-Symbolic Reasoning Framework for Task-Oriented Dialogue Generation (Yang et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.338.pdf
Software:
 2022.acl-long.338.software.zip
Code
 shiquanyang/ns-dial