D ialogue CRN : Contextual Reasoning Networks for Emotion Recognition in Conversations (2021)

Abstract: Emotion Recognition in Conversations (ERC) has gained increasing attention for developing empathetic machines. Recently, many approaches have been devoted to perceiving conversational context by deep learning models. However, these approaches are insufficient in understanding the context due to lacking the ability to extract and integrate emotional clues. In this work, we propose novel Contextual Reasoning Networks (DialogueCRN) to fully understand the conversational context from a cognitive perspective. Inspired by the Cognitive Theory of Emotion, we design multi-turn reasoning modules to extract and integrate emotional clues. The reasoning module iteratively performs an intuitive retrieving process and a conscious reasoning process, which imitates human unique cognitive thinking. Extensive experiments on three public benchmark datasets demonstrate the effectiveness and superiority of the proposed model.

## introduction emotion recognition in conversation (erc) aims to detect emotions expressed by the speakers in each utterance of the conversation. the task is an important topic for developing empathetic machines @xcite in a variety of areas including social opinion mining @xcite , intelligent assistant @xcite , health care @xcite , and so on. a conversation often contains contextual clues @xcite that trigger the current utterance's emotion, such as the cause or situation. recent context-based works @xcite @xcite on erc have been devoted to perceiving situation-level or speaker-level context by deep learning models. however, these methods are insufficient in understanding the context that usually contains rich emotional clues. we argue they mainly suffer from the following challenges. 1) the extraction of emotional clues. most approaches @xcite generally retrieve the relevant context from a static memory, which limits the ability to capture richer emotional clues. 2) the integration of emotional clues. many works @xcite @xcite usually use the attention mechanism to integrate encoded emotional clues, ignoring their intrinsic semantic order. it would lose logical relationships between clues, making it difficult to capture key factors that trigger emotions. the cognitive theory of emotion @xcite suggests that cognitive factors are potently determined for the formation of emotional states. these cognitive factors can be captured by iteratively performing the intuitive retrieving process and conscious reasoning process in our brains @xcite @xcite . motivated by them, this paper attempts to model both critical processes to reason emotional clues and sufficiently understand the conversational context. by following the mechanism of working memory @xcite in the cognitive phase, we can iteratively perform both cognitive processes to guide the extraction and integration of emotional clues, which imitates human unique cognitive thinking. in this work, we propose novel contextual reasoning networks (dialoguecrn) to recognize the utterance's emotion by sufficiently understanding the conversational context. the model introduces a cognitive phase to extract and integrate emotional clues from the context retrieved by the perceive phase. firstly, in the perceptive phase, we leverage long short-term memory (lstm) @xcite networks to capture situation-level and speaker-level context. based on the above context, global memories can be obtained to storage different contextual information. sec-ondly, in the cognitive phase, we design multi-turn reasoning modules to iteratively extract and integrate the emotional clues. the reasoning module performs two processes, i.e., an intuitive retrieving process and a conscious reasoning process. the former utilizes the attention mechanism to match relevant contextual clues by retrieving static global memories, which imitates the intuitive retrieving process. the latter adopts lstm networks to learn intrinsic logical order and integrate contextual clues by retaining and updating dynamic working memory, which imitates the conscious reasoning process. it is slower but with human-unique rationality @xcite . finally, according to the above contextual clues at situation-level and speaker-level, an emotion classifier is used to predict the emotion label of the utterance. to evaluate the performance of the proposed model, we conduct extensive experiments on three public benchmark datasets, i.e., iemocap, se-maine and meld datasets. results consistently demonstrate that our proposed model significantly outperforms comparison methods. moreover, understanding emotional clues from a cognitive perspective can boost the performance of emotion recognition. the main contributions of this work are summarized as follows: ## conclusion this paper has investigated cognitive factors for the task of emotion recognition in conversations (erc). we propose novel contextual reasoning networks (dialoguecrn) to sufficiently understand both situation-level and speaker-level context. di-aloguecrn introduces the cognitive phase to extract and integrate emotional clues from context retrieved by the perceptive phase. in the cognitive phase, we design multi-turn reasoning modules to iteratively perform the intuitive retrieving process and conscious reasoning process, which imitates human unique cognitive thinking. finally, emotional clues that trigger the current emotion are successfully obtained and used for better classification. experiments on three benchmark datasets have proved the effectiveness and superiority of the proposed model. the case study shows that considering cognitive factors can better understand emotional clues and boost the performance of erc.