Target-Oriented Dialogue (TOD) remains a significant challenge in the LLM
era, where strategic dialogue planning is crucial for directing conversations
toward specific targets. Jedoch, existing dialogue planning methods generate
dialogue plans in a step-by-step sequential manner, and may suffer from
compounding errors and myopic actions. Um diese Einschränkungen zu beseitigen, we
introduce a novel dialogue planning framework, DiffTOD, which leverages
diffusion models to enable non-sequential dialogue planning. DiffTOD formulates
dialogue planning as a trajectory generation problem with conditional guidance,
and leverages a diffusion language model to estimate the likelihood of the
dialogue trajectory. To optimize the dialogue action strategies, DiffTOD
introduces three tailored guidance mechanisms for different target types,
offering flexible guidance towards diverse TOD targets at test time. Extensive
experiments across three diverse TOD settings show that DiffTOD can effectively
perform non-myopic lookahead exploration and optimize action strategies over a
long horizon through non-sequential dialogue planning, and demonstrates strong
flexibility across complex and diverse dialogue scenarios. Our code and data
are accessible through https://anonymous.4open.science/r/DiffTOD.

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