Recognizing Emotion Cause in Conversations

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