Knowledge Aware Emotion Recognition in Textual Conversations via Multi-Task Incremental Transformer | |
Zhang, Duzhen1,2; Chen, Xiuyi1,2; Xu, Shuang2; Xu, Bo1,2 | |
2020-12 | |
会议日期 | 2020-12 |
会议地点 | Barcelona, Spain (Online) |
英文摘要 |
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. |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/48920] |
专题 | 数字内容技术与服务研究中心_听觉模型与认知计算 |
作者单位 | 1.School of Artificial Intelligence, University of Chinese Academy of Sciences 2.Institute of Automation, Chinese Academy of Sciences (CASIA). Beijing, China |
推荐引用方式 GB/T 7714 | Zhang, Duzhen,Chen, Xiuyi,Xu, Shuang,et al. Knowledge Aware Emotion Recognition in Textual Conversations via Multi-Task Incremental Transformer[C]. 见:. Barcelona, Spain (Online). 2020-12. |
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