Joint Multi-level Attentional Model for Emotion Detection and Emotion-cause Pair Extraction

Emotion detection (ED) and emotion-cause pair extraction (ECPE) have drawn extensive research interests due to their wide applications in real-world scenarios. However, existing work fails to capture the implicit connection between two tasks. This limits the performances of these tasks. To address this issue, we propose a novel joint framework to take full advantage of the clause-level and word-level information about ED and ECPE tasks. Specifically, we explore a multi-level attentional module to model the relationship between two clauses in an emotion-cause pair. Results on two benchmark datasets show that our proposed model achieves the current best performance, outperforming the previous methods and strong neural baselines by a large margin. Our code is available at https://github.com/tomsonsgs/LVE-joint-MANN-master.