Multi-Task Learning Framework for Extracting Emotion Cause Span and Entailment in Conversations

7 Nov 2022  ·  Ashwani Bhat, Ashutosh Modi ·

Predicting emotions expressed in text is a well-studied problem in the NLP community. Recently there has been active research in extracting the cause of an emotion expressed in text. Most of the previous work has done causal emotion entailment in documents. In this work, we propose neural models to extract emotion cause span and entailment in conversations. For learning such models, we use RECCON dataset, which is annotated with cause spans at the utterance level. In particular, we propose MuTEC, an end-to-end Multi-Task learning framework for extracting emotions, emotion cause, and entailment in conversations. This is in contrast to existing baseline models that use ground truth emotions to extract the cause. MuTEC performs better than the baselines for most of the data folds provided in the dataset.

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Causal Emotion Entailment RECCON MuTE-CCEE Pos. F1 69.20 # 2
Neg. F1 85.90 # 8
Macro F1 77.55 # 6


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