Towards Emotion Cause Generation in Natural Language Processing using Deep Learning

Abstract

Emotion Cause Analysis (ECA) has recently garnered substantial attention from the researcher community. In addition to devising various techniques to solve ECA related problems, researchers also introduced different variants of the ECA tasks such as Emotion Cause Extraction (ECE), Emotion Cause Pair Extraction (ECPE), Emotion Cause Span Extraction (ECSE). These are primarily classification tasks where the cause of the emotion and/or type of the emotion expressed in the text are identified. In this paper, we propose a new ECA related task named Emotion Cause Generation (ECG). This is a generative task that aims to generate meaningful cause for an emotion expressed in a given text. We demonstrate the viability of this newly proposed task with promising early observation.