Optimizing NLU Reranking Using Entity Resolution Signals in Multi-domain Dialog Systems

Tong Wang, Jiangning Chen, Mohsen Malmir, Shuyan Dong, Xin He, Han Wang, Chengwei Su, Yue Liu, Yang Liu


Abstract
In dialog systems, the Natural Language Understanding (NLU) component typically makes the interpretation decision (including domain, intent and slots) for an utterance before the mentioned entities are resolved. This may result in intent classification and slot tagging errors. In this work, we propose to leverage Entity Resolution (ER) features in NLU reranking and introduce a novel loss term based on ER signals to better learn model weights in the reranking framework. In addition, for a multi-domain dialog scenario, we propose a score distribution matching method to ensure scores generated by the NLU reranking models for different domains are properly calibrated. In offline experiments, we demonstrate our proposed approach significantly outperforms the baseline model on both single-domain and cross-domain evaluations.
Anthology ID:
2021.naacl-industry.3
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers
Month:
June
Year:
2021
Address:
Online
Editors:
Young-bum Kim, Yunyao Li, Owen Rambow
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
19–25
Language:
URL:
https://aclanthology.org/2021.naacl-industry.3
DOI:
10.18653/v1/2021.naacl-industry.3
Bibkey:
Cite (ACL):
Tong Wang, Jiangning Chen, Mohsen Malmir, Shuyan Dong, Xin He, Han Wang, Chengwei Su, Yue Liu, and Yang Liu. 2021. Optimizing NLU Reranking Using Entity Resolution Signals in Multi-domain Dialog Systems. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers, pages 19–25, Online. Association for Computational Linguistics.
Cite (Informal):
Optimizing NLU Reranking Using Entity Resolution Signals in Multi-domain Dialog Systems (Wang et al., NAACL 2021)
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PDF:
https://aclanthology.org/2021.naacl-industry.3.pdf
Video:
 https://aclanthology.org/2021.naacl-industry.3.mp4