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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.

EMNLP 2024

Unsupervised Extraction of Dialogue Policies from Conversations

action-localization

multimodal ai

representation learning

poster

## Welcome to EMNLP 2024! 
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EMNLP 2024 will take place in Miami, Florida from Nov 12th to Nov 16th, 2024, at the Hyatt Regency Miami Hote and on Underline for remote participants.

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FoodieQA: A Multimodal Dataset for Fine-Grained Understanding of Chinese Food Culture

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Scope-enhanced Compositional Semantic Parsing for DRT

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ATPO: Automatic Tree-Structured Prompt Optimization

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Advancing Social Intelligence in AI Agents: Technical Challenges and Open Question

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Rethinking the Role of Proxy Rewards in Language Model Alignment

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Generative Subgraph Retrieval for Knowledge Graph–Grounded Dialog Generation

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