Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, pages 165–176, Hong Kong, China, November 3–7, 2019. c⃝2019 Association for Computational Linguistics 165 Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations Peixiang Zhong1,2, Di Wang1, Chunyan Miao1,2,3 1Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly 2Alibaba-NTU Singapore Joint Research Institute 3School of Computer Science and Engineering Nanyang Technological University, Singapore peixiang001@e.ntu.edu.sg, {wangdi, ascymiao}@ntu.edu.sg Abstract Messages in human conversations inherently convey emotions. The task of detecting emo- tions in textual conversations leads to a wide range of applications such as opinion mining in social networks. However, enabling ma- chines to analyze emotions in conversations is challenging, partly because humans often rely on the context and commonsense knowledge to express emotions. In this paper, we address these challenges by proposing a Knowledge- Enriched Transformer (KET), where contex- tual utterances are interpreted using hierarchi- cal self-attention and external commonsense knowledge is dynamically leveraged using a context-aware affective graph attention mech- anism. Experiments on multiple textual con- versation datasets demonstrate that both con- text and commonsense knowledge are consis- tently beneficial to the emotion detection per- formance. In addition, the experimental results show that our KET model outperforms the state-of-the-art models on most of the tested datasets in F1 score. 1 Introduction Emotions are “generated states in humans that re- flect evaluative judgments of the environment, the self and other social agents” (Hudlicka, 2011). Messages in human communications inherently convey emotions. With the prevalence of social media platforms such as Facebook Messenger, as well as conversational agents such as Amazon Alexa, there is an emerging need for machines to understand human emotions in natural conversa- tions. This work addresses the task of detecting emotions (e.g., happy, sad, angry, etc.) in textual conversations, where the emotion of an utterance is detected in the conversational context. Being able to effectively detect emotions in conversa- tions leads to a wide range of applications rang- ing from opinion mining in social media platforms No emotion socialize What do you plan to do for your birthday? I want to have a picnic with my friends, Mum. How about a party at home? That way we can get together and celebrate it. OK, Mum. I'll invite my friends home. party movie No emotion Happiness Happiness Context Response Figure 1: An example conversation with annotated la- bels from the DailyDialog dataset (Li et al., 2017). By referring to the context, “it” in the third utterance is linked to “birthday” in the first utterance. By lever- aging an external knowledge base, the meaning of “friends” in the forth utterance is enriched by associ- ated knowledge entities, namely “socialize”, “party”, and “movie”. Thus, the implicit “happiness” emotion in the fourth utterance can be inferred more easily via its enriched meaning. (Chatterjee et al., 2019) to building emotion-aware conversational agents (Zhou et al., 2018a). However, enabling machines to analyze emo- tions in human conversations is challenging, partly …

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