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        <title>All Learning Has an Emotional Basis, So Does Task-Oriented Dialogue</title>
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        <namePart type="given">Shutong</namePart>
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        <namePart type="given">Hsien-chin</namePart>
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    <name type="personal">
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    <abstract>Task-oriented dialogue (ToD) systems aim to help users accomplish goals through natural language interaction. Beyond task success, effective ToD systems must also maintain positive emotional interaction and accurately convey information in inherently noisy and ambiguous conversational environments. Recent advances in large language models (LLMs) have substantially improved conversational fluency and contextual understanding. However, although emotion has been incorporated into existing ToD systems, its integration is typically confined to limited roles, restricting its influence on overall dialogue behaviour and user experience. To address this, we propose a novel end-to-end framework that combines fully lexicalised representations with an LLM backbone for improved semantic accuracy and employs online reinforcement learning with short-term emotional and long-term task-success rewards for joint optimisation of emotional interaction and task success. We further develop a challenging and realistic simulation environment that enables systematic evaluation across diverse ToD set-ups. Experiments show that incorporating coarse affective signals improves task success and sentiment rating in aggregate. Furthermore, integrating emotional interaction throughout the dialogue system using our proposed framework results in more pronounced improvements in both task success and user emotional experience.1</abstract>
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    <identifier type="doi">10.1162/tacl.a.812</identifier>
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        <date>2026</date>
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%0 Journal Article
%T All Learning Has an Emotional Basis, So Does Task-Oriented Dialogue
%A Feng, Shutong
%A Lin, Hsien-chin
%A Lubis, Nurul
%A van Niekerk, Carel
%A Heck, Michael
%A Ruppik, Benjamin
%A Vukovic, Renato
%A Gašić, Milica
%J Transactions of the Association for Computational Linguistics
%D 2026
%V 14
%I MIT Press
%C Cambridge, MA
%F feng-etal-2026-learning
%X Task-oriented dialogue (ToD) systems aim to help users accomplish goals through natural language interaction. Beyond task success, effective ToD systems must also maintain positive emotional interaction and accurately convey information in inherently noisy and ambiguous conversational environments. Recent advances in large language models (LLMs) have substantially improved conversational fluency and contextual understanding. However, although emotion has been incorporated into existing ToD systems, its integration is typically confined to limited roles, restricting its influence on overall dialogue behaviour and user experience. To address this, we propose a novel end-to-end framework that combines fully lexicalised representations with an LLM backbone for improved semantic accuracy and employs online reinforcement learning with short-term emotional and long-term task-success rewards for joint optimisation of emotional interaction and task success. We further develop a challenging and realistic simulation environment that enables systematic evaluation across diverse ToD set-ups. Experiments show that incorporating coarse affective signals improves task success and sentiment rating in aggregate. Furthermore, integrating emotional interaction throughout the dialogue system using our proposed framework results in more pronounced improvements in both task success and user emotional experience.1
%R 10.1162/tacl.a.812
%U https://aclanthology.org/2026.tacl-1.115/
%U https://doi.org/10.1162/tacl.a.812
%P 2475-2499

Markdown (Informal)

[All Learning Has an Emotional Basis, So Does Task-Oriented Dialogue](https://aclanthology.org/2026.tacl-1.115/) (Feng et al., TACL 2026)

• All Learning Has an Emotional Basis, So Does Task-Oriented Dialogue (Feng et al., TACL 2026)

ACL

• Shutong Feng, Hsien-chin Lin, Nurul Lubis, Carel van Niekerk, Michael Heck, Benjamin Ruppik, Renato Vukovic, and Milica Gašić. 2026. All Learning Has an Emotional Basis, So Does Task-Oriented Dialogue. Transactions of the Association for Computational Linguistics, 14:2475–2499.