LEEETs-Dial: Linguistic Entrainment in End-to-End Task-oriented Dialogue systems

• URL: http://arxiv.org/abs/2311.09390v2
• Date: Thu, 4 Apr 2024 11:26:17 GMT
• Title: LEEETs-Dial: Linguistic Entrainment in End-to-End Task-oriented Dialogue systems
• Authors: Nalin Kumar, Ondřej Dušek,
• Abstract summary: We introduce methods for achieving dialogue entrainment in a GPT-2-based end-to-end task-oriented dialogue system. We experiment with training instance weighting, entrainment-specific loss, and additional conditioning to generate responses that align with the user.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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