CAN - GRU : a Hierarchical Model for Emotion Recognition in Dialogue (2020)

Abstract: Emotion recognition in dialogue systems has gained attention in the field of natural language processing recent years, because it can be applied in opinion mining from public conversational data on social media. In this paper, we propose a hierarchical model to recognize emotions in the dialogue. In the first layer, in order to extract textual features of utterances, we propose a convolutional self-attention network(CAN). Convolution is used to capture n-gram information and attention mechanism is used to obtain the relevant semantic information among words in the utterance. In the second layer, a GRU-based network helps to capture contextual information in the conversation. Furthermore, we discuss the effects of unidirectional and bidirectional networks. We conduct experiments on Friends dataset and EmotionPush dataset. The results show that our proposed model(CAN-GRU) and its variants achieve better performance than baselines.

## introduction as an important component of human intelligence, emotional intelligence is defined as the ability to perceive, integrate, understand and regulate emotions @xcite . emotion is the essential difference between human and machine, so emotion understanding is an important research direction of artificial intelligence. as the most common way for people to communicate in daily life, dialogue contains a wealth of emotions. recognising the emotions in the conversation is of great significance in intelligent customer service, medical systems, education systems and other aspects. according to @xcite , textual features usually contain more emotional information than video or audio features, so we focus on the emotion analysis of dialogue text and aims to recognize the emotion of each utterrance in dialogues. there are some challenges in this task. first, the length of an utterance may be too long, making it difficult to capture contextual information. furthermore, a dialogue usually contains lots of utterances, therefore, it's hard to grasp long-term contextual relations between utterances. second, the same word may express different emotions in different contexts. for example, in ## related work text emotion recognition is one of the most hot topic in natural language processing. recent years,a lot of classical neural networks are used to tackle this problem. such as long short-term memory network @xcite , gated recurrent unit network @xcite and textual convolutional neural network @xcite . however, these models don't perform well when the texts are too long, because it's hard to capture the long-range contextual information. later, attention mechanism @xcite is proposed to solve this problem. recently, self-attention @xcite is widely used since it can solve the long-term dependence problem of text effectively. recent years, more and more researchers focus on emotion recognition in conversation. this task aims to recognize the emotion of each utterance in dialogues. bclstm @xcite extracts textual features by cnn and model the sequence of utterances by lstm. considering inter-speaker dependency relations, conversational memory network(cmn) @xcite has been proposed to model the speaker-based emotion using memory network and summarize task-specific details by attention mechanisms. icon @xcite improves the cmn, it hierarchically models the self-speaker emotion and inter-speaker emotion into global memories. dialoguernn @xcite uses emotion gru and global gru to model inter-party relation, and uses party gru to model relation between two sequential states of the same party. dialoguegcn @xcite improves dialoguernn by graph convolutional network, and it can hold richer context relevant to emotion. however, these models may be too complex for small textual dialogue datasets. in this paper, we study on the emotionx challenge @xcite , dialogue emotion recognition challenge, which aims to recognize the emotion of each utterance in dialogues. according to the overview of this task, the best team @xcite proposes a cnn-dcnn auto encoder based model, which includes a convolutional encoder and a deconvolutional decoder. the second place team @xcite mainly uses bilstm with a self-attentive architecture on the top for the classiffication. the third place team @xcite proposes a hierarchical network based on attention models and conditional random fields(crf). for a meaningful comparison, we use the same dataset and metric as the challenge in our study. ## conclusion in the paper, we propose a hierarchical model(can-gru) to tackle emotion recognition in dialogues. unlike existing works, we focus on semantic information extraction within utterance in the dialogue. n-gram features and relevant semantic information among words in the utterance are learned by the convolutional self-attention network in the first layer and the sequence of utterances is modeled by the gru-based network in the second layer. we improve can-gru to three variants, can-bigru, can-grua and can-bigrua. experimental results show that attention mechanism can help to grasp longterm dependency in the contexts effectively. can-bigrua achieves better results than can-grua demonstrates that it is necessary to consider both past and future information of the utterance.in the future, we will try to explore deeper semantic information in the context and focus more on emotion shift to solve the problem of poor performance of the model in complex situations.