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        <title>Unsupervised Extraction of Dialogue Policies from Conversations</title>
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        <namePart type="given">Makesh</namePart>
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    <abstract>Dialogue policies play a crucial role in developing task-oriented dialogue systems, yet their development and maintenance are challenging and typically require substantial effort from experts in dialogue modeling. While in many situations, large amounts of conversational data are available for the task at hand, people lack an effective solution able to extract dialogue policies from this data. In this paper, we address this gap by first illustrating how Large Language Models (LLMs) can be instrumental in extracting dialogue policies from datasets, through the conversion of conversations into a unified intermediate representation consisting of canonical forms. We then propose a novel method for generating dialogue policies utilizing a controllable and interpretable graph-based methodology. By combining canonical forms across conversations into a flow network, we find that running graph traversal algorithms helps in extracting dialogue flows. These flows are a better representation of the underlying interactions than flows extracted by prompting LLMs. Our technique focuses on giving conversation designers greater control, offering a productivity tool to improve the process of developing dialogue policies.</abstract>
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%0 Conference Proceedings
%T Unsupervised Extraction of Dialogue Policies from Conversations
%A Sreedhar, Makesh Narsimhan
%A Rebedea, Traian
%A Parisien, Christopher
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F sreedhar-etal-2024-unsupervised
%X Dialogue policies play a crucial role in developing task-oriented dialogue systems, yet their development and maintenance are challenging and typically require substantial effort from experts in dialogue modeling. While in many situations, large amounts of conversational data are available for the task at hand, people lack an effective solution able to extract dialogue policies from this data. In this paper, we address this gap by first illustrating how Large Language Models (LLMs) can be instrumental in extracting dialogue policies from datasets, through the conversion of conversations into a unified intermediate representation consisting of canonical forms. We then propose a novel method for generating dialogue policies utilizing a controllable and interpretable graph-based methodology. By combining canonical forms across conversations into a flow network, we find that running graph traversal algorithms helps in extracting dialogue flows. These flows are a better representation of the underlying interactions than flows extracted by prompting LLMs. Our technique focuses on giving conversation designers greater control, offering a productivity tool to improve the process of developing dialogue policies.
%U https://aclanthology.org/2024.emnlp-main.1060
%P 19029-19045

Markdown (Informal)

[Unsupervised Extraction of Dialogue Policies from Conversations](https://aclanthology.org/2024.emnlp-main.1060) (Sreedhar et al., EMNLP 2024)

• Unsupervised Extraction of Dialogue Policies from Conversations (Sreedhar et al., EMNLP 2024)

ACL

• Makesh Narsimhan Sreedhar, Traian Rebedea, and Christopher Parisien. 2024. Unsupervised Extraction of Dialogue Policies from Conversations. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 19029–19045, Miami, Florida, USA. Association for Computational Linguistics.