Recognizing Emotion Cause in Conversations

TL;DR

This paper introduces the task of recognizing emotion causes in conversations, provides a new dataset called RECCON, and establishes strong Transformer-based baselines that outperform previous methods, advancing explainable emotion-aware AI.

Contribution

The paper defines the novel task of emotion cause recognition in conversations, introduces the RECCON dataset, and develops Transformer-based models for causal span extraction and entailment.

Findings

Transformer baselines outperform previous approaches

The RECCON dataset contains over 1,000 dialogues with cause-effect pairs

Strong baselines set a new standard for future research

Abstract

We address the problem of recognizing emotion cause in conversations, define two novel sub-tasks of this problem, and provide a corresponding dialogue-level dataset, along with strong Transformer-based baselines. The dataset is available at https://github.com/declare-lab/RECCON. Introduction: Recognizing the cause behind emotions in text is a fundamental yet under-explored area of research in NLP. Advances in this area hold the potential to improve interpretability and performance in affect-based models. Identifying emotion causes at the utterance level in conversations is particularly challenging due to the intermingling dynamics among the interlocutors. Method: We introduce the task of Recognizing Emotion Cause in CONversations with an accompanying dataset named RECCON, containing over 1,000 dialogues and 10,000 utterance cause-effect pairs. Furthermore, we define different cause types based on the source of the causes, and establish strong Transformer-based baselines to address two different sub-tasks on this dataset: causal span extraction and causal emotion entailment. Result: Our Transformer-based baselines, which leverage contextual pre-trained embeddings, such as RoBERTa, outperform the state-of-the-art emotion cause extraction approaches Conclusion: We introduce a new task highly relevant for (explainable) emotion-aware artificial intelligence: recognizing emotion cause in conversations, provide a new highly challenging publicly available dialogue-level dataset for this task, and give strong baseline results on this dataset.