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.

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Recognizing Emotion Cause in Conversations RECCON SpanBERT Exact Span F1 34.64 # 1
F1 75.71 # 1
F1(Pos) 60.00 # 1
F1(Neg) 86.02 # 1
Causal Emotion Entailment RECCON RoBERTa Base Pos. F1 64.28 # 7
Neg. F1 88.74 # 6
Macro F1 76.51 # 8
Causal Emotion Entailment RECCON RoBERTa Large Pos. F1 66.23 # 6
Neg. F1 87.89 # 7
Macro F1 77.06 # 7
Recognizing Emotion Cause in Conversations RECCON RoBERTa Base Exact Span F1 32.63 # 2
F1 75.45 # 2
F1(Pos) 58.17 # 2
F1(Neg) 85.85 # 2