Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

11-2025

Abstract

Understanding user intentions in multi-turn dialogues is critical for conversational AI, yet existing approaches—relying on rigid slot-value structures or unstructured free-text—fail to fully capture conversational complexity. In this paper, we propose IntentionFrame, a semi-structured framework inspired by psychological and cognitive intention theories, which organizes conversational intents into four interrelated aspects: situation, emotion, action, and knowledge. This design not only retains interpretability but also provides LLMs with a rich context to accurately parse and respond to nuanced user inputs. To efficiently scale IntentionFrame annotations, we introduce a Weakly-supervised Reinforced Generation (WeRG) method that leverages a small set of high-quality human annotations in conjunction with abundant coarsely labeled data. By applying reinforcement learning to balance these diverse signals, WeRG aims to effectively generate reliable IntentionFrame annotations, which serve as essential grounding for downstream tasks—leading to substantial improvements in response generation and task completion. Our experiments, supported by both automatic metrics and human evaluations, show that integrating IntentionFrame with WeRG significantly improves LLMs’ conversational understanding and sets a new benchmark for intent analysis.

Discipline

Artificial Intelligence and Robotics

Areas of Excellence

Digital transformation

Publication

Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, Suzhou, China (EMNLP 2025), November 4-9

First Page

28108

Last Page

28125

Identifier

10.18653/v1/2025.emnlp-main.1427

Publisher

ACL

City or Country

China

Additional URL

https://doi.org/10.18653/v1/2025.emnlp-main.1427

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