# Multi-Hypergraph Neural Networks for Emotion Recognition in Multi-Party Conversations
## 2 Related Work  
### Emotion Recognition in Conversations  
In the following paragraphs, we divide the related works into two categories according to their methods to model a conversation's context. Note that, here, we regard the network by using Transformer [23] without actually building a graph as a model based on a recurrent neural network. DialogXL [24], BERT+MTL [14], and ERMC-DisGCN [6] have been used for some research on emotion recognition in multi-party conversations.  
**Recurrence-Based Models.** ICON [8] separately models each speaker's historical utterances through GRU [25] and uses an additional GRU to model the impacts between speakers. DialogueRNN [3] uses three GRUs to model the speaker, the context given by the preceding utterances, and the emotion behind the preceding utterances, respectively. COSMIC [13], which was built on DialogueRNN, uses commonsense knowledge (CSK) to learn interactions among participating interlocutors. BiERU [10] involved the design of a generalized neural tensor block (GNTB) to generate contextual utterance vectors by taking the context and current utterance as inputs, and then extracting features from the contextual utterance vector by using a two-channel model (LSTM [26] and CNN [27]). EmoCaps [28] introduced the concept of emotion vectors into multi-modal emotion recognition and involved the proposal of a new emotion feature extraction structure, Emformer. BERT+MTL [14] exploits speaker identification as an auxiliary task to enhance the representation of utterances in conversations. DialogueCRN [12] is used to understand the conversational context from a cognitive perspective and integrates emotional cues through a multi-turn reasoning module for classification. VAE-ERC [29] models the context-aware latent utterance role with a latent variable to overcome the lack of utterance role annotation in ERC datasets. TODKAT [30] proposes a new model in which the transformer model fuses topical knowledge and CSK to predict the emotion label. DialogXL [24] improves XLNet [31] with enhanced memory and dialog-aware self-attention. CoG-BART [32] utilizes supervised contrastive learning in ERC and incorporates response generation as an auxiliary task when certain contextual information is involved.  
**Graph-Based Models.** DialogueGCN [5] used a context window to connect the current utterance with surrounding utterances and treated each dialogue as a graph. RGAT [15]  
Figure 1: Conversation as a hypergraph. Circles and triangles represent nodes and hyperedges, respectively. A. B, C are participants in the conversation.  
used BERT [33] to obtain contextual information and proposed relational position encodings to use the location information of utterances. ConGCN [34] regarded both speakers and utterances as graph nodes and the whole dataset as one graph. DAG-ERC [17] involved the design of a directed acyclic graph neural network and provided a method for modeling the information flow between the remote context and local context. SKAIG [18] utilized CSK to enrich edges with knowledge representations and process a graph structure with a graph transformer. MMGCN [35] was the proposal of a new model based on a multi-modal fused graph convolutional network. TUCORE-GCN [36] proposed a context-aware graph convolutional network model by focusing on how people understand conversations. ERMC-DisGCN [6] involved the design of a relational convolution to leverage the speaker self-dependency of interlocutors to propagate contextual information and the proposal of an utterance-aware graph neural network.