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        <title>Multi-Domain Dialogue State Tracking with Top-K Slot Self Attention</title>
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            <title>Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue</title>
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    <abstract>As an important component of task-oriented dialogue systems, dialogue state tracking is designed to track the dialogue state through the conversations between users and systems. Multi-domain dialogue state tracking is a challenging task, in which the correlation among different domains and slots needs to consider. Recently, slot self-attention is proposed to provide a data-driven manner to handle it. However, a full-support slot self-attention may involve redundant information interchange. In this paper, we propose a top-k attention-based slot self-attention for multi-domain dialogue state tracking. In the slot self-attention layers, we force each slot to involve information from the other k prominent slots and mask the rest out. The experimental results on two mainstream multi-domain task-oriented dialogue datasets, MultiWOZ 2.0 and MultiWOZ 2.4, present that our proposed approach is effective to improve the performance of multi-domain dialogue state tracking. We also find that the best result is obtained when each slot interchanges information with only a few slots.</abstract>
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%0 Conference Proceedings
%T Multi-Domain Dialogue State Tracking with Top-K Slot Self Attention
%A Yang, Longfei
%A Li, Jiyi
%A Li, Sheng
%A Shinozaki, Takahiro
%Y Lemon, Oliver
%Y Hakkani-Tur, Dilek
%Y Li, Junyi Jessy
%Y Ashrafzadeh, Arash
%Y Garcia, Daniel Hernández
%Y Alikhani, Malihe
%Y Vandyke, David
%Y Dušek, Ondřej
%S Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2022
%8 September
%I Association for Computational Linguistics
%C Edinburgh, UK
%F yang-etal-2022-multi
%X As an important component of task-oriented dialogue systems, dialogue state tracking is designed to track the dialogue state through the conversations between users and systems. Multi-domain dialogue state tracking is a challenging task, in which the correlation among different domains and slots needs to consider. Recently, slot self-attention is proposed to provide a data-driven manner to handle it. However, a full-support slot self-attention may involve redundant information interchange. In this paper, we propose a top-k attention-based slot self-attention for multi-domain dialogue state tracking. In the slot self-attention layers, we force each slot to involve information from the other k prominent slots and mask the rest out. The experimental results on two mainstream multi-domain task-oriented dialogue datasets, MultiWOZ 2.0 and MultiWOZ 2.4, present that our proposed approach is effective to improve the performance of multi-domain dialogue state tracking. We also find that the best result is obtained when each slot interchanges information with only a few slots.
%R 10.18653/v1/2022.sigdial-1.24
%U https://aclanthology.org/2022.sigdial-1.24
%U https://doi.org/10.18653/v1/2022.sigdial-1.24
%P 231-236

Markdown (Informal)

[Multi-Domain Dialogue State Tracking with Top-K Slot Self Attention](https://aclanthology.org/2022.sigdial-1.24) (Yang et al., SIGDIAL 2022)

• Multi-Domain Dialogue State Tracking with Top-K Slot Self Attention (Yang et al., SIGDIAL 2022)

ACL

• Longfei Yang, Jiyi Li, Sheng Li, and Takahiro Shinozaki. 2022. Multi-Domain Dialogue State Tracking with Top-K Slot Self Attention. In Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 231–236, Edinburgh, UK. Association for Computational Linguistics.