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

• URL: http://arxiv.org/abs/2106.09790v1
• Date: Thu, 17 Jun 2021 20:11:04 GMT
• Title: Multi-Task Learning and Adapted Knowledge Models for Emotion-Cause Extraction
• Authors: Elsbeth Turcan, Shuai Wang, Rishita Anubhai, Kasturi Bhattacharjee, Yaser Al-Onaizan, Smaranda Muresan
• Abstract summary: 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, we propose novel methods. We show performance improvement on both tasks when including common-sense reasoning and a multitask framework.
• Score: 18.68808042388714
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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