Knowledge Aware Emotion Recognition in Textual Conversations via Multi-Task Incremental Transformer
Duzhen Zhang, Xiuyi Chen, Shuang Xu, Bo Xu · 2020
Emotion recognition in textual conversations (ERTC) plays an important role in a wide range of applications, such as opinion mining, recommender systems, and so on.ERTC, however, is a challenging task.For one thing, speakers often rely on the context and commonsense knowledge to express emotions; for another, most utterances contain neutral emotion in conversations, as a result, the confusion between a few non-neutral utterances and much more neutral ones restrains the emotion recognition performance.In this paper, we propose a novel Knowledge Aware Incremental Transformer with Multi-task Learning (KAITML) to address these challenges.Firstly, we devise a dual-level graph attention mechanism to leverage commonsense knowledge, which augments the semantic information of the utterance.Then we apply the Incremental Transformer to encode multi-turn contextual utterances.Moreover, we are the first to introduce multi-task learning to alleviate the aforementioned confusion and thus further improve the emotion recognition performance.Extensive experimental results show that our KAITML model outperforms the state-of-the-art models across five benchmark datasets.