Abstract
The dependencies between system and user utterances in the same turn and across different turns are not fully considered in existing multidomain dialogue state tracking (MDST) models. In this study, we argue that the incorporation of these dependencies is crucial for the design of MDST and propose Parallel Interactive Networks (PIN) to model these dependencies. Specifically, we integrate an interactive encoder to jointly model the in-turn dependencies and cross-turn dependencies. The slot-level context is introduced to extract more expressive features for different slots. And a distributed copy mechanism is utilized to selectively copy words from historical system utterances or historical user utterances. Empirical studies demonstrated the superiority of the proposed PIN model.- Anthology ID:
- 2020.emnlp-main.151
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1921–1931
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.151
- DOI:
- 10.18653/v1/2020.emnlp-main.151
- Cite (ACL):
- Junfan Chen, Richong Zhang, Yongyi Mao, and Jie Xu. 2020. Parallel Interactive Networks for Multi-Domain Dialogue State Generation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1921–1931, Online. Association for Computational Linguistics.
- Cite (Informal):
- Parallel Interactive Networks for Multi-Domain Dialogue State Generation (Chen et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.emnlp-main.151.pdf
- Code
- BDBC-KG-NLP/PIN_EMNLP2020