Fully statistical neural belief tracking
ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers)
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Mrkšić, N., & Vulić, I. (2018). Fully statistical neural belief tracking. ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers), 2 108-113. https://doi.org/10.18653/v1/p18-2018
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.
ECH2020 EUROPEAN RESEARCH COUNCIL (ERC) (648909)
External DOI: https://doi.org/10.18653/v1/p18-2018
This record's URL: https://www.repository.cam.ac.uk/handle/1810/285038