@inproceedings{kumar-dusek-2024-leeets,
title = "{LEEET}s-Dial: Linguistic Entrainment in End-to-End Task-oriented Dialogue systems",
author = "Kumar, Nalin and
Dusek, Ondrej",
editor = "Duh, Kevin and
Gomez, Helena and
Bethard, Steven",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2024",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-naacl.46",
pages = "727--735",
abstract = "Linguistic entrainment, or alignment, represents a phenomenon where linguistic patterns employed by conversational participants converge to one another. While entrainment has been shown to produce a more natural user experience, most dialogue systems do not have any provisions for it. In this work, we introduce methods for achieving dialogue entrainment in a GPT-2-based end-to-end task-oriented dialogue system through the utilization of shared vocabulary. We experiment with training instance weighting, entrainment-specific loss, and additional conditioning to generate responses that align with the user. We demonstrate that all three approaches produce significantly better entrainment than the base, non-entrainment-optimized model, as confirmed by both automated and manual evaluation metrics.",
}
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%0 Conference Proceedings
%T LEEETs-Dial: Linguistic Entrainment in End-to-End Task-oriented Dialogue systems
%A Kumar, Nalin
%A Dusek, Ondrej
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Findings of the Association for Computational Linguistics: NAACL 2024
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F kumar-dusek-2024-leeets
%X Linguistic entrainment, or alignment, represents a phenomenon where linguistic patterns employed by conversational participants converge to one another. While entrainment has been shown to produce a more natural user experience, most dialogue systems do not have any provisions for it. In this work, we introduce methods for achieving dialogue entrainment in a GPT-2-based end-to-end task-oriented dialogue system through the utilization of shared vocabulary. We experiment with training instance weighting, entrainment-specific loss, and additional conditioning to generate responses that align with the user. We demonstrate that all three approaches produce significantly better entrainment than the base, non-entrainment-optimized model, as confirmed by both automated and manual evaluation metrics.
%U https://aclanthology.org/2024.findings-naacl.46
%P 727-735
Markdown (Informal)
[LEEETs-Dial: Linguistic Entrainment in End-to-End Task-oriented Dialogue systems](https://aclanthology.org/2024.findings-naacl.46) (Kumar & Dusek, Findings 2024)
ACL