Attention Modulation for Zero-Shot Cross-Domain Dialogue State Tracking
Mathilde Veron, Olivier Galibert, Guillaume Bernard, Sophie Rosset
Abstract
Dialog state tracking (DST) is a core step for task-oriented dialogue systems aiming to track the user’s current goal during a dialogue. Recently a special focus has been put on applying existing DST models to new domains, in other words performing zero-shot cross-domain transfer. While recent state-of-the-art models leverage large pre-trained language models, no work has been made on understanding and improving the results of first developed zero-shot models like SUMBT. In this paper, we thus propose to improve SUMBT zero-shot results on MultiWOZ by using attention modulation during inference. This method improves SUMBT zero-shot results significantly on two domains and does not worsen the initial performance with the great advantage of needing no additional training.- Anthology ID:
- 2022.codi-1.11
- Volume:
- Proceedings of the 3rd Workshop on Computational Approaches to Discourse
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea and Online
- Venue:
- CODI
- SIG:
- Publisher:
- International Conference on Computational Linguistics
- Note:
- Pages:
- 86–91
- Language:
- URL:
- https://aclanthology.org/2022.codi-1.11
- DOI:
- Cite (ACL):
- Mathilde Veron, Olivier Galibert, Guillaume Bernard, and Sophie Rosset. 2022. Attention Modulation for Zero-Shot Cross-Domain Dialogue State Tracking. In Proceedings of the 3rd Workshop on Computational Approaches to Discourse, pages 86–91, Gyeongju, Republic of Korea and Online. International Conference on Computational Linguistics.
- Cite (Informal):
- Attention Modulation for Zero-Shot Cross-Domain Dialogue State Tracking (Veron et al., CODI 2022)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2022.codi-1.11.pdf
- Code
- mathilde-veron/attention-modulation-zero-dst