D ual GAT s: Dual Graph Attention Networks for Emotion Recognition in Conversations (2023)

Abstract: Capturing complex contextual dependencies plays a vital role in Emotion Recognition in Conversations (ERC). Previous studies have predominantly focused on speaker-aware context modeling, overlooking the discourse structure of the conversation. In this paper, we introduce Dual Graph ATtention networks (DualGATs) to concurrently consider the complementary aspects of discourse structure and speaker-aware context, aiming for more precise ERC. Specifically, we devise a Discourse-aware GAT (DisGAT) module to incorporate discourse structural information by analyzing the discourse dependencies between utterances. Additionally, we develop a Speaker-aware GAT (SpkGAT) module to incorporate speaker-aware contextual information by considering the speaker dependencies between utterances. Furthermore, we design an interaction module that facilitates the integration of the DisGAT and SpkGAT modules, enabling the effective interchange of relevant information between the two modules. We extensively evaluate our method on four datasets, and experimental results demonstrate that our proposed DualGATs surpass state-of-the-art baselines on the majority of the datasets.

## introduction with the increasing availability of conversational data on social media platforms @xcite , emotion recognition in conversations (erc) has emerged as a popular research topic @xcite . its objective is to identify and track the emotional state of each utterance. erc plays a crucial role in various applications, including opinion mining in social media @xcite and the development of empathetic dialogue systems that can analyze user emotional states and gen-erate emotion-aware responses @xcite @xcite . however, analyzing emotions in conversations poses significant challenges. unlike emotion recognition in isolated sentences @xcite , erc requires careful consideration of contextual dependencies. previous erc methods have primarily focused on capturing speaker or temporal dependencies between utterances, making the modeling of speaker-aware context central to these approaches @xcite . to incorporate speaker-aware contextual information, numerous methods have been proposed to model conversations as sequences @xcite @xcite @xcite or graphs @xcite @xcite . sequence-based methods capture sequential information by encoding utterances temporally using recurrent neural networks (rnns). @xcite designed an independent gated recurrent unit (gru) @xcite to track the emotional state of the speaker. however, these sequence-based methods often rely on limited information from nearby utterances to update the current utterance's representation, making it challenging to capture distant contextual information and achieve satisfactory performance. to address this limitation, graph-based methods simultaneously aggregate information from surrounding contextual utterances to update the representation of the current utterance using graph neural networks (gnns) @xcite . these methods typically treat the conversation as a directed graph, where nodes represent utterances, edges indicate dependency links between pairs of nodes, and edge labels denote the dependency types, such as speaker or temporal relationships. despite the remarkable progress made by sequence-based and graph-based methods, there is a need for greater emphasis on explicitly model- in this paper, we propose a novel method called dual graph attention networks (dualgats) that aims to improve the accuracy of erc by simultaneously considering the complementarity of discourse structure and speaker-aware context. the dualgats layer comprises three components: discourse-aware gat (disgat), speaker-aware gat (spkgat), and an interaction module. the disgat module is designed to capture structurallevel correlations among the interactive turns explicitly. it propagates the message over the discourse dependency graph obtained from a discourse parser @xcite , thereby incor-porating discourse structural information. on the other hand, the spkgat module is implicitly organized to capture semantic-level correlations among the interactive turns. it conducts message propagation over the speaker dependency graph, constructed based on speaker identities and the relative positions of utterances, enabling the incorporation of speaker-aware contextual information. furthermore, inspired by previous work @xcite , the interaction module leverages mutual cross-attention to integrate the disgat and spkgat modules, facilitating the exchange of relevant information between the two modules. to enhance the complementarity of the learned representations from the disgat and spkgat modules and minimize overlap, the interaction module also includes a differential regularizer. this regularizer encourages the two modules to capture different contextual information. our contributions can be summarized as follows: ## methodology we begin by providing a formal definition of the erc task. a conversation is represented as a sequence of utterances (u i , s i )|@xmath0, ..., n , where each utterance u i is spoken by speaker s i , and n denotes the total number of utterances. the objective of the erc task is to assign an emotion label y @xmath1, such as joy, sadness, etc., to each utterance u i in the conversation, where y represents the set of possible emotion labels. the proposed dualgats consist of three main components: feature extraction, dualgats layer, and emotion prediction. the overall architecture of our dualgats is illustrated in figure 2 . ## conclusion in this paper, we propose dualgats with dis-gat, spkgat, and interaction modules to simultaneously consider the discourse structure's and speaker-aware context's complementarity for more accurate erc. the disgat and spkgat incorporate discourse structural and speaker-aware contextual information in parallel. the subsequent interaction module integrates the disgat and spkgat and effectively exchanges relevant information between the two modules via mutual cross-attention. experimental results show that our dualgats outperform previous state-of-the-art baselines on most tested datasets, and further analysis validates the effectiveness of critical modules in dualgats. in the future, we will explore the following aspects: (1) apply our method to similar tasks that need to incorporate the discourse structural and speaker-aware contextual information; (2) enhance the ability of our method to handle class imbalance or similar emotion problems, such as the introduction of data augmentation or contrastive learning techniques; (3) deal with domain gap problem when directly using pre-trained deep sequential models to parse conversations in erc datasets @xcite .