Emotion-Cause Joint Detection: A Unified Network with Dual Interaction for Emotion Cause Analysis.

Emotion cause analysis has attracted much attention in the field of natural language processing. The existing works include emotion cause extraction (ECE) and emotion-cause pair extraction (ECPE), but the former requires emotion annotations, thereby restricting its application scenarios, and the latter consists of two steps in sequence, thereby making the second step depend on the results of first step. To tackle the limits, we implement emotion detection and cause detection as two sub-tasks in a unified framework. Based on this framework, we propose an emotion-cause joint detection (ECJD) method, which enhances the interaction of sub-tasks in a synchronous and joint way to improve performance. Specifically, we formalize ECE as a four-class classification problem, in which clause representation is evaluated from the dual perspective of both emotion and cause. We implement cause detection with consideration of relative position from emotion detection as prior knowledge so as to improve detection performance. The experimental evaluation based on an emotion cause corpus benchmark shows that our method achieves the best performance of cause detection without using emotion annotations and overcomes the limits of ECE and ECPE, and further demonstrates the effectiveness of our model.