Knowledge Aware Emotion Recognition in Textual Conversations via Multi-Task Incremental Transformer

By Duzhen Zhang, Xiuyi Chen, Shuang Xu, Bo Xu. 2020

53 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Experimental and Cognitive Psychology

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In Artificial Intelligence

1. Zhen Fang
2. Xiao Wang
3. Lixu Wang
4. Qi Zhu
5. Shichao Xu
6. Jiahua Dong
7. Gan Sun

Citations in the corpus

• Federated Class-Incremental Learning (2022, background): “Class-Incremental Learning (CIL) aims to learn new classes continuously while tackling forgetting on old classes [1, 19, 54].”