Multi-Domain Dialogue State Tracking By Neural-Retrieval Augmentation
Lohith Ravuru, Seonghan Ryu, Hyungtak Choi, Haehun Yang, Hyeonmok Ko
Abstract
Dialogue State Tracking (DST) is a very complex task that requires precise understanding and information tracking of multi-domain conversations between users and dialogue systems. Many task-oriented dialogue systems use dialogue state tracking technology to infer users’ goals from the history of the conversation. Existing approaches for DST are usually conditioned on previous dialogue states. However, the dependency on previous dialogues makes it very challenging to prevent error propagation to subsequent turns of a dialogue. In this paper, we propose Neural Retrieval Augmentation to alleviate this problem by creating a Neural Index based on dialogue context. Our NRA-DST framework efficiently retrieves dialogue context from the index built using a combination of unstructured dialogue state and structured user/system utterances. We explore a simple pipeline resulting in a retrieval-guided generation approach for training a DST model. Experiments on different retrieval methods for augmentation show that neural retrieval augmentation is the best performing retrieval method for DST. Our evaluations on the large-scale MultiWOZ dataset show that our model outperforms the baseline approaches.- Anthology ID:
- 2022.findings-aacl.16
- Volume:
- Findings of the Association for Computational Linguistics: AACL-IJCNLP 2022
- Month:
- November
- Year:
- 2022
- Address:
- Online only
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 169–175
- Language:
- URL:
- https://aclanthology.org/2022.findings-aacl.16
- DOI:
- Cite (ACL):
- Lohith Ravuru, Seonghan Ryu, Hyungtak Choi, Haehun Yang, and Hyeonmok Ko. 2022. Multi-Domain Dialogue State Tracking By Neural-Retrieval Augmentation. In Findings of the Association for Computational Linguistics: AACL-IJCNLP 2022, pages 169–175, Online only. Association for Computational Linguistics.
- Cite (Informal):
- Multi-Domain Dialogue State Tracking By Neural-Retrieval Augmentation (Ravuru et al., Findings 2022)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2022.findings-aacl.16.pdf