Abstract
Emotion is intrinsic to humans and consequently, emotion understanding is a key part of human-like artificial intelligence (AI). Emotion recognition in conversation (ERC) is becoming increasingly popular as a new research frontier in natural language processing (NLP) due to its ability to mine opinions from the plethora of publicly available conversational data on platforms such as Facebook, Youtube, Reddit, Twitter, and others. Moreover, it has potential applications in health-care systems (as a tool for psychological analysis), education (understanding student frustration), and more. In Addition, ERC is also extremely important for generating emotion-aware dialogues that require an understanding of the user's emotions. Catering to these needs calls for effective and scalable conversational emotion-recognition algorithms. However, it is a difficult problem to solve because of several research challenges. In this paper, we discuss these challenges and shed light on recent research in this field. We also describe the drawbacks of these approaches and discuss the reasons why they fail to successfully overcome the research challenges in ERC.
Highlights
Emotion is often defined as an individual’s mental state associated with thoughts, feelings, and behavior
Hazarika et al [23] improved upon this approach with interactive conversational memory network (ICON), which interconnects these memories to model self and inter-speaker emotional influence
We summarized the recent advances in this task and highlight several key research challenges associated with this research area
Summary
Emotion is often defined as an individual’s mental state associated with thoughts, feelings, and behavior. Evolutionary theories of emotion were initiated in the late 19th century by Darwin and Prodger [1]. Unlike vanilla emotion recognition of sentences/ utterances, ERC ideally requires context modeling of the individual utterances. This context can be attributed to the preceding utterances, and relies on the temporal sequence of utterances. Compared to the recently published works on ERC [10]–[12], both lexicon-based [8], [13], [14] and modern deep learning-based [4], [5] vanilla emotion recognition approaches fail to work well on ERC datasets as these works ignore the conversation specific factors such as the presence.
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