M u CDN : Mutual Conversational Detachment Network for Emotion Recognition in Multi-Party Conversations (2022)

Abstract: As an emerging research topic in natural language processing community, emotion recognition in multi-party conversations has attained increasing interest. Previous approaches that focus either on dyadic or multi-party scenarios exert much effort to cope with the challenge of emotional dynamics and achieve appealing results. However, since emotional interactions among speakers are often more complicated within the entangled multi-party conversations, these works are limited in capturing effective emotional clues in conversational context. In this work, we propose Mutual Conversational Detachment Network (MuCDN) to clearly and effectively understand the conversational context by separating conversations into detached threads. Specifically, two detachment ways are devised to perform context and speaker-specific modeling within detached threads and they are bridged through a mutual module. Experimental results on two datasets show that our model achieves better performance over the baseline models.

## introduction emotion recognition in conversations (erc) is a task that predicts the emotion for each utterance in conversations. since it plays a significant role in achieving empathetic systems and is of great values to be applied in the fields of opinion mining in conversations, social media analysis, mental health care and other areas, erc has received more and more attention in the natural language processing (nlp) community. we focus on emotion recognition in multi-party conversations (ermc) where three or more speakers are involved. the challenge of erc, especially for that of ermc, lies in a complicated emotional interaction referred to as emotional dynamics @xcite ) that utterances from one speaker would have impact on expressions of others. to cope * corresponding author with the challenge, many previous approaches exploit recurrent neural network (rnn) @xcite and graph neural network (gnn) @xcite @xcite to perform context and speaker-specific modeling. however, they are not effective enough to capture exact emotional clues for emotion recognition because the salient characteristic of multi-party conversations is ignored. multiple conversation threads are intermingled in the same dialog history simultaneously in the multi-party scenario @xcite , which makes it hard to capture emotional clues in such a complex and confusing interaction. as shown in figure 1 , there are two conversation threads in a multi-party conversation consisting of three speakers a, b and c. targeting at the same utterance #2 from speaker b to reply, speaker a is consoled and becomes joyful to go shopping, while speaker c is unbearable towards the outfit of b and feels disgusted. since speaker c and a originally focus on different aspects of the same dialog history, two individual threads are naturally developed. it is of great necessity to figure out the intra-and inter-personal dependencies of utterances within distinct conversation threads, for the convenience of understanding the exact emotional interaction exerted on different speakers. in this paper, in order to clearly comprehend emotional clues for emotion recognition, we propose mutual conversational detachment network (mucdn) to separate a multi-party conversation into detached threads and effectively perform context and speaker-specific modeling within them. to be more specific, two detachment ways, named explicit detachment and implicit detachment are devised to separate a multi-party conversation into several threads and jointly carry out context and speaker-specific modeling within them. for the former one, we detach a multi-party conversation with the help of a dialog discourse parser. along with paths of a discourse tree, detached conversation threads could be explicitly attained. then two speaker-aware gated neural networks (gru) are adopted for conversational information propagating. further, implicit detachment aims at capturing the latent and global interaction in the conversation. we construct two speaker-specific implicit detachment graphs and utilize self-attention mechanism to obtain detached threads implicitly. in addition, a mutual module is designed to create the interaction between explicit detachment and implicit detachment. to evaluate the performance of mucdn, we conduct extensive experiments on two ermc datasets and new state-of-the-art performance is achieved on both of them. the main contributions of this work are summarized as follows: ## conclusion in this paper, in order to capture emotional clues for emotion dynamics in a clear and effective way under the circumstance of multi-party scenario, we propose mutual conversational detachment network (mucdn) for emotion recognition in multiparty conversations. joint context and speakerspecific modeling is performed within individual detached threads by two detachment ways and a mutual module. experimental results on two ermc datasets demonstrate the superiority of our proposed mucdn. also, by conducting comprehensive evaluations and ablation study, we confirmed the effectiveness of our mucdn and the impact of its components. for future work, we would like to explore effective techniques to prune the dialogue discourse tree to alleviate error propagation. moreover, for a more comprehensive speaker-specific modeling, richer speaker information in the conversational context should be excavated further.