COSMIC : CO mmon S ense knowledge for e M otion Identification in Conversations (2020)

Abstract: In this paper, we address the task of utterance level emotion recognition in conversations using commonsense knowledge. We propose COSMIC, a new framework that incorporates different elements of commonsense such as mental states, events, and causal relations, and build upon them to learn interactions between interlocutors participating in a conversation. Current state-of-theart methods often encounter difficulties in context propagation, emotion shift detection, and differentiating between related emotion classes. By learning distinct commonsense representations, COSMIC addresses these challenges and achieves new state-of-the-art results for emotion recognition on four different benchmark conversational datasets. Our code is available at https://github.com/declare-lab/conv-emotion .

## introduction emotion recognition is a long-standing research problem in artificial intelligence (ai). with the growing popularity of conversational ai research, the topic of emotion recognition in conversations has received significant attention from the research community @xcite @xcite . identifying emotions in conversations is a core step toward fine-grained conversation understanding, which in turn is essential for downstream tasks such as emotion-aware chat agents @xcite , visual question answering @xcite , health conversations @xcite and others. natural conversations are complex as they are governed by several distinct variables that affect the flow of a conversation and the emotional dynamics of the participants. these variables include reaction of a: gets tired reaction of b: irritated effect on b: gets yelled at angry angry person a person b look, it's a beautiful day outside, why are we arguing? well, what do you want me to do about it? what do you want? reaction of a: angry, annoyed intent of b: help out effect on b: thinks what to do commonsense inference commonsense inference i want you to pretend like he's coming back. ## related work emotion recognition has been an active area of research for many years and has been explored across inter-disciplinary fields such as machine learning, signal processing, social and cognitive psychology, etc @xcite . the seminal work from @xcite presented findings on facial expressions, methods to measure facial expression and their relation with human emotion. acoustic information and visual cues were later used for emotion recognition by @xcite . however, emotion recognition in conversations has gained popularity only recently due to the emergence of publicly available conversational datasets collected from social media platforms and scripted situations such as movies and tv-shows @xcite . the main approach towards conversational emotion recognition is to perform contextual modeling in either textual or multimodal setting with deep-learning based algorithms. @xcite used recurrent neural networks for multimodal emotion recognition followed by @xcite , where party and global states were used for modeling the emotional dynamics. an external knowledge base was used in @xcite with transformer networks to perform emotion recognition. some of the other important works include @xcite @xcite . ## conclusion in this work, we presented cosmic, a framework that models various aspects of commonsense knowledge by considering mental states, events, actions, and cause-effect relations for emotion recognition in conversations. using commonsense representations, our model alleviates issues such as difficulty in detecting emotion shifts and misclassification between related emotion classes that are often present in current rnn and gcn based methods. cosmic achieves new state-of-the-art results for emotion recognition across several benchmark datasets.