From Chatter to Matter: Addressing Critical Steps of Emotion Recognition Learning in Task-oriented Dialogue

TL;DR

This paper introduces a framework to adapt chit-chat emotion recognition models for task-oriented dialogues, improving accuracy by addressing data, features, and objectives, and demonstrating strong zero-shot generalization.

Contribution

It proposes novel methods for augmenting emotions, incorporating dialogue states, and multi-task learning with emotion-distance weighting for ERC in task-oriented dialogues.

Findings

Significant performance improvements on EmoWOZ dataset.

Effective emotion augmentation techniques for rare emotions.

Strong zero-shot generalization to other task-oriented dialogue datasets.

Abstract

Emotion recognition in conversations (ERC) is a crucial task for building human-like conversational agents. While substantial efforts have been devoted to ERC for chit-chat dialogues, the task-oriented counterpart is largely left unattended. Directly applying chit-chat ERC models to task-oriented dialogues (ToDs) results in suboptimal performance as these models overlook key features such as the correlation between emotions and task completion in ToDs. In this paper, we propose a framework that turns a chit-chat ERC model into a task-oriented one, addressing three critical aspects: data, features and objective. First, we devise two ways of augmenting rare emotions to improve ERC performance. Second, we use dialogue states as auxiliary features to incorporate key information from the goal of the user. Lastly, we leverage a multi-aspect emotion definition in ToDs to devise a multi-task learning objective and a novel emotion-distance weighted loss function. Our framework yields significant improvements for a range of chit-chat ERC models on EmoWOZ, a large-scale dataset for user emotion in ToDs. We further investigate the generalisability of the best resulting model to predict user satisfaction in different ToD datasets. A comparison with supervised baselines shows a strong zero-shot capability, highlighting the potential usage of our framework in wider scenarios.