Pseudo-Siamese Network for Planning in Target-Oriented Proactive Dialogues
This paper proposes a Forward-Focused Bidirectional Pseudo-Siamese Network (FF-BPSN) for dialogue path planning in target-oriented proactive dialogue systems. FF-BPSN uses two identical transformer-based decoders for forward and backward planning, integrated by a forward-focused module to construct the final forward path. Experiments on DuRecDial and DuRecDial 2.0 show state-of-the-art performance, significantly enhancing the effectiveness of target-oriented proactive dialogue systems. The paper is accepted at ICASSP2026.
[2605.20195] Pseudo-Siamese Network for Planning in Target-Oriented Proactive Dialogues
[Submitted on 4 Apr 2026]
Title:Pseudo-Siamese Network for Planning in Target-Oriented Proactive Dialogues
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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 path to guide language models in response generation. Extensive experiments on DuRecDial and DuRecDial 2.0 demonstrate that FF-BPSN achieves state-of-the-art performance in dialogue path planning and significantly enhances the effectiveness of target-oriented proactive dialogue systems.
Comments: ICASSP2026
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2605.20195 [cs.CL]
(or arXiv:2605.20195v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.20195
arXiv-issued DOI via DataCite
Submission history
From: Maodong Li [view email] [v1] Sat, 4 Apr 2026 08:24:10 UTC (521 KB)
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