Abstract
This paper proposes an improvement to the existing data-driven Neural Belief Tracking (NBT) framework for Dialogue State Tracking (DST). The existing NBT model uses a hand-crafted belief state update mechanism which involves an expensive manual retuning step whenever the model is deployed to a new dialogue domain. We show that this update mechanism can be learned jointly with the semantic decoding and context modelling parts of the NBT model, eliminating the last rule-based module from this DST framework. We propose two different statistical update mechanisms and show that dialogue dynamics can be modelled with a very small number of additional model parameters. In our DST evaluation over three languages, we show that this model achieves competitive performance and provides a robust framework for building resource-light DST models.- Anthology ID:
- P18-2018
- Volume:
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Editors:
- Iryna Gurevych, Yusuke Miyao
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 108–113
- Language:
- URL:
- https://aclanthology.org/P18-2018
- DOI:
- 10.18653/v1/P18-2018
- Cite (ACL):
- Nikola Mrkšić and Ivan Vulić. 2018. Fully Statistical Neural Belief Tracking. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 108–113, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Fully Statistical Neural Belief Tracking (Mrkšić & Vulić, ACL 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/P18-2018.pdf
- Code
- nmrksic/neural-belief-tracker