# Multi-Hypergraph Neural Networks for Emotion Recognition in Multi-Party Conversations
## 1 Introduction  
Emotion recognition in conversations (ERC) has attracted more and more attention because of the prevalence of dialogue behavior in various fields. The primary purpose of ERC is to recognize the emotion of each utterance in the dialogue. The recognized emotion can be used for opinion mining on social media, such as Facebook and Instagram, building conversational assistants, and conducting medical psychoanalysis [1; 2; 3; 4]. However, ERC, especially emotion recognition in multi-party conversations (ERMC), often exhibits more difficulties than traditional text sentiment analysis due to the emotional dynamics of dialogue [4]. There are two kinds of emotional dependencies among the participants in a dialogue--inter-dependency and self-dependency. Self-dependency is the influence of what the speaker says on the current utterance. Inter-dependency is the influence of what others say on what the current speaker says. Therefore, identifying the emotion of an utterance in a multi-party dialogue depends not only on the utterance itself and its context, but also on the speaker's self-dependence and the inter-dependency [5; 6]. Existing work on emotion recognition in conversations can be roughly divided into two categories: that based on recurrent neural networks and that based on graph neural networks. Some recent works based on recurrent neural networks [3; 7; 8; 9; 10; 11; 12; 13] began to focus on conversational context modeling and speaker-specific modeling, and some works [14] have even carried out multi-task learning for speaker-specific modeling on this basis. They tried to deal with speaker-dependent influences through speaker-specific modeling and conversational context modeling, but they could use other speakers' utterances to influence the current utterance well. Meanwhile, some works based on graph neural networks [5; 6; 15; 16; 17; 18] have used relational graph convolutional networks (RGCNs) [19] todistinguish among different speaker dependencies, and some have even used conversational discourse structure [6] or commonsense knowledge [18] to extend relationships among utterances. These models are intended to establish more perfect utterance relations and then aggregate according to the relations to form the influence of the surrounding utterances on the current utterance. However, the performance of such models is affected by the type and quantity of inter-utterance relations. Moreover, an emotional change in a speaker may be caused by the joint influence of multiple utterances of multiple speakers. This influence may also be caused by the interactions of utterances in different relationships. So, inter-dependency is more complex than self-dependency. We believe that it is necessary to build a graph network alone to model inter-dependency, especially for multi-dialogue, and this can allow better identification of the problem of emotional shifts between consecutive utterances of the same speaker. Conventional graph neural networks focus on pairwise relationships between objects in the constructed graphs. In many real-world scenarios, however, relationships among objects are not dyadic (pairwise), but rather triadic, tetradic, or higher. Squeezing the high-order relations into pairwise ones leads to information loss and impedes expressiveness [20]. So, we used a hypergraph neural network [21] to deal with two kinds of speaker dependencies instead of using a conventional graph neural network. According to the hypergraph [21] structure, we know that a hyperedge may contain multiple utterances, and an utterance may belong to multiple hyperedges. We let each utterance generate a hyperedge. The nodes on the hyperedge are the corresponding current utterance and the specific context of the current utterance. Hypergraph neural networks [21] can use the structure of a hypergraph to deal with the influences of multiple utterances from multiple speakers on an utterance, that is, they use multiple surrounding utterances to produce an influence on the current utterance.