Multi-Task Learning and Adapted Knowledge Models for Emotion-Cause Extraction

Detecting what emotions are expressed in text is a well-studied problem in natural language processing. However, research on finer grained emotion analysis such as what causes an emotion is still in its infancy. We present solutions that tackle both emotion recognition and emotion cause detection in...

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Bibliographic Details
Published in:arXiv.org 2021-06
Main Authors: Turcan, Elsbeth, Wang, Shuai, Anubhai, Rishita, Bhattacharjee, Kasturi, Al-Onaizan, Yaser, Muresan, Smaranda
Format: Article
Language:English
Subjects:
Emotion recognition
Emotions
Learning
Natural language processing
Online Access:Get full text
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Summary:Detecting what emotions are expressed in text is a well-studied problem in natural language processing. However, research on finer grained emotion analysis such as what causes an emotion is still in its infancy. We present solutions that tackle both emotion recognition and emotion cause detection in a joint fashion. Considering that common-sense knowledge plays an important role in understanding implicitly expressed emotions and the reasons for those emotions, we propose novel methods that combine common-sense knowledge via adapted knowledge models with multi-task learning to perform joint emotion classification and emotion cause tagging. We show performance improvement on both tasks when including common-sense reasoning and a multitask framework. We provide a thorough analysis to gain insights into model performance.
ISSN:2331-8422