@inproceedings{coca-etal-2023-grounding,
title = "Grounding Description-Driven Dialogue State Trackers with Knowledge-Seeking Turns",
author = "Coca, Alexandru and
Tseng, Bo-Hsiang and
Chen, Jinghong and
Lin, Weizhe and
Zhang, Weixuan and
Anders, Tisha and
Byrne, Bill",
editor = "Stoyanchev, Svetlana and
Joty, Shafiq and
Schlangen, David and
Dusek, Ondrej and
Kennington, Casey and
Alikhani, Malihe",
booktitle = "Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
month = sep,
year = "2023",
address = "Prague, Czechia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.sigdial-1.42",
doi = "10.18653/v1/2023.sigdial-1.42",
pages = "444--456",
abstract = "Schema-guided dialogue state trackers can generalise to new domains without further training, yet they are sensitive to the writing style of the schemata. Augmenting the training set with human or synthetic schema paraphrases improves the model robustness to these variations but can be either costly or difficult to control. We propose to circumvent these issues by grounding the state tracking model in knowledge-seeking turns collected from the dialogue corpus as well as the schema. Including these turns in prompts during finetuning and inference leads to marked improvements in model robustness, as demonstrated by large average joint goal accuracy and schema sensitivity improvements on SGD and SGD-X.",
}
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<abstract>Schema-guided dialogue state trackers can generalise to new domains without further training, yet they are sensitive to the writing style of the schemata. Augmenting the training set with human or synthetic schema paraphrases improves the model robustness to these variations but can be either costly or difficult to control. We propose to circumvent these issues by grounding the state tracking model in knowledge-seeking turns collected from the dialogue corpus as well as the schema. Including these turns in prompts during finetuning and inference leads to marked improvements in model robustness, as demonstrated by large average joint goal accuracy and schema sensitivity improvements on SGD and SGD-X.</abstract>
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%0 Conference Proceedings
%T Grounding Description-Driven Dialogue State Trackers with Knowledge-Seeking Turns
%A Coca, Alexandru
%A Tseng, Bo-Hsiang
%A Chen, Jinghong
%A Lin, Weizhe
%A Zhang, Weixuan
%A Anders, Tisha
%A Byrne, Bill
%Y Stoyanchev, Svetlana
%Y Joty, Shafiq
%Y Schlangen, David
%Y Dusek, Ondrej
%Y Kennington, Casey
%Y Alikhani, Malihe
%S Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2023
%8 September
%I Association for Computational Linguistics
%C Prague, Czechia
%F coca-etal-2023-grounding
%X Schema-guided dialogue state trackers can generalise to new domains without further training, yet they are sensitive to the writing style of the schemata. Augmenting the training set with human or synthetic schema paraphrases improves the model robustness to these variations but can be either costly or difficult to control. We propose to circumvent these issues by grounding the state tracking model in knowledge-seeking turns collected from the dialogue corpus as well as the schema. Including these turns in prompts during finetuning and inference leads to marked improvements in model robustness, as demonstrated by large average joint goal accuracy and schema sensitivity improvements on SGD and SGD-X.
%R 10.18653/v1/2023.sigdial-1.42
%U https://aclanthology.org/2023.sigdial-1.42
%U https://doi.org/10.18653/v1/2023.sigdial-1.42
%P 444-456
Markdown (Informal)
[Grounding Description-Driven Dialogue State Trackers with Knowledge-Seeking Turns](https://aclanthology.org/2023.sigdial-1.42) (Coca et al., SIGDIAL 2023)
ACL