  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.
