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        <title>Transition-Matrix Regularization for Next Dialogue Act Prediction in Counselling Conversations</title>
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            <namePart type="given">Jiajun</namePart>
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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Transition-Matrix Regularization for Next Dialogue Act Prediction in Counselling Conversations
%A Rudolph, Eric
%A Steigerwald, Philipp
%A Albrecht, Jens
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F rudolph-etal-2026-transition
%X 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.
%U https://aclanthology.org/2026.findings-acl.1271/
%P 25442-25457

Markdown (Informal)

[Transition-Matrix Regularization for Next Dialogue Act Prediction in Counselling Conversations](https://aclanthology.org/2026.findings-acl.1271/) (Rudolph et al., Findings 2026)

• Transition-Matrix Regularization for Next Dialogue Act Prediction in Counselling Conversations (Rudolph et al., Findings 2026)

ACL

• Eric Rudolph, Philipp Steigerwald, and Jens Albrecht. 2026. Transition-Matrix Regularization for Next Dialogue Act Prediction in Counselling Conversations. In Findings of the Association for Computational Linguistics: ACL 2026, pages 25442–25457, San Diego, California, United States. Association for Computational Linguistics.