This paper studies how empirical dialogue-flow statistics can be incorporated into Next Dialogue Act Prediction (NDAP). A KL regularization term is proposed that aligns predicted act distributions with corpus-derived transition patterns. Evaluated on a 60-class German counselling taxonomy using 5-fold cross-validation, this improves macro-F1 by 9--42% relative depending on encoder and substantially improves dialogue-flow alignment. Cross-dataset validation on HOPE suggests that improvements transfer across languages and counselling domains. In systematic ablations across pretrained encoders and architectures, the findings indicate that transition regularization provides consistent gains and disproportionately benefits weaker baseline models. The results suggest that lightweight discourse-flow priors complement pretrained encoders, especially in fine-grained, data-sparse dialogue tasks.

翻译：本文研究如何将经验性对话流统计信息融入下一对话行为预测（Next Dialogue Act Prediction, NDAP）。我们提出一种KL正则化项，旨在对齐预测的行为分布与语料库导出的转移模式。在采用5折交叉验证的60类德语咨询分类体系上评估，该方法根据不同编码器使宏F1值相对提升9%-42%，并显著改进了对话流对齐效果。在HOPE数据集上进行的跨数据集验证表明，该改进具有跨语言和跨咨询领域的迁移性。通过对预训练编码器与架构的系统性消融实验，发现转移正则化能提供稳定增益，且对较弱基线模型的提升尤为显著。研究结果表明，轻量级对话流先验知识可有效补充预训练编码器，尤其在细粒度、数据稀疏的对话任务中表现突出。