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

Ashwani Bhat, Ashutosh Modi
Proceedings of The 1st Transfer Learning for Natural Language Processing Workshop, PMLR 203:33-51, 2023.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v203-bhat23a, title = {Multi-Task Learning Framework for Extracting Emotion Cause Span and Entailment in Conversations}, author = {Bhat, Ashwani and Modi, Ashutosh}, booktitle = {Proceedings of The 1st Transfer Learning for Natural Language Processing Workshop}, pages = {33--51}, year = {2023}, editor = {Albalak, Alon and Zhou, Chunting and Raffel, Colin and Ramachandran, Deepak and Ruder, Sebastian and Ma, Xuezhe}, volume = {203}, series = {Proceedings of Machine Learning Research}, month = {03 Dec}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v203/bhat23a/bhat23a.pdf}, url = {https://proceedings.mlr.press/v203/bhat23a.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T Multi-Task Learning Framework for Extracting Emotion Cause Span and Entailment in Conversations %A Ashwani Bhat %A Ashutosh Modi %B Proceedings of The 1st Transfer Learning for Natural Language Processing Workshop %C Proceedings of Machine Learning Research %D 2023 %E Alon Albalak %E Chunting Zhou %E Colin Raffel %E Deepak Ramachandran %E Sebastian Ruder %E Xuezhe Ma %F pmlr-v203-bhat23a %I PMLR %P 33--51 %U https://proceedings.mlr.press/v203/bhat23a.html %V 203 %X 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.
APA
Bhat, A. & Modi, A.. (2023). Multi-Task Learning Framework for Extracting Emotion Cause Span and Entailment in Conversations. Proceedings of The 1st Transfer Learning for Natural Language Processing Workshop, in Proceedings of Machine Learning Research 203:33-51 Available from https://proceedings.mlr.press/v203/bhat23a.html.

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