Static and Dynamic Speaker Modeling based on Graph Neural Network for Emotion Recognition in Conversation (2022)

Abstract: Each person has a unique personality which affects how they feel and convey emotions. Hence, speaker modeling is important for the task of emotion recognition in conversation (ERC). In this paper, we propose a novel graph-based ERC model which considers both conversational context and speaker personality. We model the internal state of the speaker (personality) as Static and Dynamic speaker state, where the Dynamic speaker state is modeled with a graph neural network based encoder. Experiments on benchmark dataset shows the effectiveness of our model. Our model outperforms baseline and other graph-based methods. Analysis of results also show the importance of explicit speaker modeling.

## introduction emotion recognition in conversation (erc) is a task within the sphere of emotion recognition. erc aims to predict the emotion of each utterance in a conversation. with the recent advances of dialogue research, erc has gained popularity due to its potential to support downstream applications such as affective dialog systems @xcite and opinion mining from social media chats @xcite . the emotion of an utterance depends on many factors including surrounding context and speaker personality. previous studies show that the same utterance can express different emotions under different contexts @xcite . on the other hand, the speaker's personality and background should be considered when we interpret the emotion of an utterance. for example, in figure 1 , the utterance "this is great!" can carry the emotion of anger (sarcastic person) or joy (not sarcastic). this difference can be attributed to the different personalities of the speakers. in speaker modeling, we aim to model the internal state of the speaker. moreover, we distinguish figure 1 : the emotion conveyed by the phrase "this is great" can either be anger (sarcasm) or joy (in the case that the person ordered the wrong item). this example is taken from @xcite . ## related work dialoggcn @xcite was the first paper to use gnn to model dialogues. given an input dialogue, a complete graph within a fixed context (past and future) window is built. since graph-based neural networks do not take sequential information into account, rgat @xcite uses relational positional encodings to improve upon dialoggcn. dag-erc @xcite built a more intuitive graph structure by considering local and remote information, without using any future utterance. emoberta (kim and vossen, 2021) modeled the speaker state and context by prepending the speaker names to utterances and inserting separation tokens between the utterances in a dialogue, and feeding it to roberta. congcn @xcite explicitly used speaker nodes, which were initialized randomly. mmgcn @xcite also incorporated randomly initialized speaking embeddings in their model. ## methodology our model consists of three components: feature extractor, graph encoder, and prediction layer. fig -ure 2 shows an overview of our proposed model. in this section, we introduce the construction of a 127 dialogue graph and the details of the graph encoder. ## conclusion we proposed a novel graph-based method to model speaker states explicitly for the task of erc. experiments showed that our model outperforms baselines and other graph-based models. we analyse the impact of speaker modeling and show that both static speaker state and dynamic speaker state modeling are important for the accurate prediction of emotions in erc. in addition, we investigate the 5