Multi-Task Learning and Adapted Knowledge Models for Emotion-Cause Extraction
This addresses the problem of finer-grained emotion analysis for natural language processing applications, but it is incremental as it builds on existing emotion recognition methods.
The paper tackled joint emotion recognition and emotion cause detection by combining common-sense knowledge models with multi-task learning, showing performance improvements on both tasks.
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.