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        <title>TOAD: Task-Oriented Automatic Dialogs with Diverse Response Styles</title>
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    <abstract>In light of recent advances in large language models (LLMs), the expectations for the next generation of virtual assistants include enhanced naturalness and adaptability across diverse usage scenarios. However, the creation of high-quality annotated data for Task-Oriented Dialog (TOD) is recognized to be slow and costly. To address these challenges, we introduce Task-Oriented Automatic Dialogs (TOAD), a novel and scalable TOD dataset along with its automatic generation pipeline. The TOAD dataset simulates realistic app context interaction and provide a variety of system response style options. Two aspects of system response styles are considered, verbosity level and users’ expression mirroring. We benchmark TOAD on two response generation tasks, and the results show that modeling more verbose responses or responses without user expression mirroring is more challenging.</abstract>
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%0 Conference Proceedings
%T TOAD: Task-Oriented Automatic Dialogs with Diverse Response Styles
%A Liu, Yinhong
%A Fang, Yimai
%A Vandyke, David
%A Collier, Nigel
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F liu-etal-2024-toad
%X In light of recent advances in large language models (LLMs), the expectations for the next generation of virtual assistants include enhanced naturalness and adaptability across diverse usage scenarios. However, the creation of high-quality annotated data for Task-Oriented Dialog (TOD) is recognized to be slow and costly. To address these challenges, we introduce Task-Oriented Automatic Dialogs (TOAD), a novel and scalable TOD dataset along with its automatic generation pipeline. The TOAD dataset simulates realistic app context interaction and provide a variety of system response style options. Two aspects of system response styles are considered, verbosity level and users’ expression mirroring. We benchmark TOAD on two response generation tasks, and the results show that modeling more verbose responses or responses without user expression mirroring is more challenging.
%U https://aclanthology.org/2024.findings-acl.494
%P 8341-8356

Markdown (Informal)

[TOAD: Task-Oriented Automatic Dialogs with Diverse Response Styles](https://aclanthology.org/2024.findings-acl.494) (Liu et al., Findings 2024)

• TOAD: Task-Oriented Automatic Dialogs with Diverse Response Styles (Liu et al., Findings 2024)

ACL

• Yinhong Liu, Yimai Fang, David Vandyke, and Nigel Collier. 2024. TOAD: Task-Oriented Automatic Dialogs with Diverse Response Styles. In Findings of the Association for Computational Linguistics ACL 2024, pages 8341–8356, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.