Pseudo-Siamese Network for Planning in Target-Oriented Proactive Dialogues
Xinyue Kang, Maodong Li, Yibin Zheng, Fang Kong

TL;DR
This paper introduces a novel transformer-based pseudo-Siamese network for planning dialogue paths in target-oriented proactive systems, improving response relevance and system effectiveness.
Contribution
It proposes the FF-BPSN model that combines bidirectional planning with forward focus, advancing dialogue path planning for proactive systems.
Findings
Achieves state-of-the-art in dialogue path planning on DuRecDial datasets.
Significantly improves response relevance in target-oriented dialogues.
Enhances overall system effectiveness in proactive dialogue tasks.
Abstract
A target-oriented proactive dialogue system is designed to steer conversations toward predefined targets while actively providing suggestions. The core paradigm of such a system is to plan a reasonable dialogue path and subsequently guide language models (e.g., pre-trained or large language models) to generate responses, where dialogue path planning serves as the central component-a novel yet under-explored problem. In this work, we propose a Forward-Focused Bidirectional Pseudo-Siamese Network (FF-BPSN) for dialogue path planning toward predefined dialogue targets. FF-BPSN employs two identical transformer-based decoders for forward and backward planning, together with a forward-focused module that integrates bidirectional information to construct the final forward path. This path benefits from bidirectional planning while prioritizing forward information. We then employ the planned…
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