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        <title>Attention Modulation for Zero-Shot Cross-Domain Dialogue State Tracking</title>
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            <title>Proceedings of the 3rd Workshop on Computational Approaches to Discourse</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T Attention Modulation for Zero-Shot Cross-Domain Dialogue State Tracking
%A Veron, Mathilde
%A Galibert, Olivier
%A Bernard, Guillaume
%A Rosset, Sophie
%Y Braud, Chloe
%Y Hardmeier, Christian
%Y Li, Junyi Jessy
%Y Loaiciga, Sharid
%Y Strube, Michael
%Y Zeldes, Amir
%S Proceedings of the 3rd Workshop on Computational Approaches to Discourse
%D 2022
%8 October
%I International Conference on Computational Linguistics
%C Gyeongju, Republic of Korea and Online
%F veron-etal-2022-attention
%X 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.
%U https://aclanthology.org/2022.codi-1.11
%P 86-91

Markdown (Informal)

[Attention Modulation for Zero-Shot Cross-Domain Dialogue State Tracking](https://aclanthology.org/2022.codi-1.11) (Veron et al., CODI 2022)

• Attention Modulation for Zero-Shot Cross-Domain Dialogue State Tracking (Veron et al., CODI 2022)

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.