Relation-aware Graph Attention Networks with Relational Position Encodings for Emotion Recognition in Conversations (2020)

Abstract: Interest in emotion recognition in conversations (ERC) has been increasing in various fields, because it can be used to analyze user behaviors and detect fake news. Many recent ERC methods use graph-based neural networks to take the relationships between the utterances of the speakers into account. In particular, the state-of-the-art method considers self- and inter-speaker dependencies in conversations by using relational graph attention networks (RGAT). However, graph-based neural networks do not take sequential information into account. In this paper, we propose relational position encodings that provide RGAT with sequential information reflecting the relational graph structure. Accordingly, our RGAT model can capture both the speaker dependency and the sequential information. Experiments on four ERC datasets show that our model is beneficial to recognizing emotions expressed in conversations. In addition, our approach empirically outperforms the state-of-the-art on all of the benchmark datasets.

## introduction interest in emotion recognition in conversations (erc) has been increasing in various fields @xcite , because it can be used to analyze user behaviors @xcite and detect fake news @xcite . with the recent proliferation of social media platforms such as facebook, twitter, and youtube, as well as conversational assistants such as amazon alexa, there is a need to study how emotions are expressed in natural conversation. recent research on erc processes the utterances of dialogues in sequence by using recurrent neural network (rnn)-based methods @xcite @xcite . however, these methods are not @xcite , which contains emotion-labeled utterances in multi-party conversations. the state-of-the-art method, dialoguegcn @xcite , uses relational graph attention networks (rgat) to take the dependency into account; it is inspired by relational graph convolutional networks (rgcn) @xcite and graph attention networks (gat) @xcite . this method takes into account the conversational context by using a directed graph, where the nodes denote individual utterances, the edges represent relationships between pairs of nodes (utterances), and the labels of the edges represent the types of relationships. however, graph-based neural networks do not take sequential information contained in utterances into account. a common response to this issue is to encode information about absolute position features @xcite or relative position features @xcite , where these encodings are added to nodes (utterances) or edges (relationships). however, in order to account for self-and inter-speaker dependency, our model focuses on relation types rather than nodes (utterances) and edges (relationships); thus, our position encoding also focuses on relation types. in this paper, we propose novel position encodings (relational position encodings) that provide the rgat model with sequential information reflecting relation types. by using the relational position encodings, our rgat model can capture both the speaker dependency and the sequential information. experiments on four erc benchmark datasets showed that our relational position encoding outperformed baselines and state-of-the-art methods. in addition, our method outperformed both the absolute and relative position encodings. in summary, our contributions are as follows: graph neural network graph-based neural networks are used in various tasks. the fundamental model is the graph convolutional network (gcn) (kipf and welling, 2016), which uses a fixed adjacency matrix as the edge weight. our method is based on rgcn @xcite and gat @xcite . the rgcn model prepares a different structure for each relation type and hence considers selfdependency and inter-speaker dependency separately. the gat model uses an attention mechanism to attend to the neighborhood's representations of the utterances. position encodings in our work, positional information is added to the graphical structure. several studies add position encodings to several structures, such as self-attention networks (sans) and gcn. sans @xcite perform the attention operation under the position-unaware assumption, in which the positions of the input are ignored. in response to this issue, the absolute position @xcite or relative position @xcite , or structure position @xcite are used to capture the sequential order of the input. similarly, graph-based neural netthen, we modify this embedding by using rgat to consider speaker dependency. the position encodings in the rgat structure take sequential information into account. finally, after concatenating the contextual embedding to the output embedding through rgat, we classify the concatenated vector into emotion labels by using a fully connected feed-forward network. ## method first, we define the problem of the erc task. the task is to recognize emotion labels (happy, sad, neutral, angry, excited, and frustrated) of utterances u 1 , u 2 , • • • , u n , where n denotes the number of utterances in a conversation. let s m for @xmath0, • • • , m be a collection of speakers in a given conversational dataset, where m denotes the number of speakers. the utterance u i is uttered by speaker s m , where m is the correspondence between the utterance and its speaker. our framework consists of three componentscontextual utterance embedding, speaker dependency modeling with position encodings and emotion classification. the entire model architecture is shown in figure 1 . although our method is based on the dialoguegcn @xcite model, it considers the positional information contained in utterances in a sequential conversation as described in section 3.2.3, whereas the dia-loguegcn model does not. ## conclusion we proposed relational position encodings for rgat to recognize human emotions in textual conversation. we incorporated the relational position encodings in the rgat structure to capture both speaker dependency and the sequential order of utterances. on four erc datasets, our model improved recognition performance over those of the baselines and existing state-of-the-art methods. additional experimental studies demonstrated that the relational position encoding approach outperformed the other position encodings and showed that it is robust to changes in window size. in future studies, we plan to increase the number of dimensions of the relational position encodings, since a scalar value may not be able to express positional information adequately.