A Multichannel Neural Network for Imbalanced Emotion Recognition Imbalanced issue becomes one of major bottleneck for further popularizing of emotion recognition in actual applications. Recently, some resampling methods have been proposed to improve performance by balancing the training samples. However, oversampling methods may lead to overfitting, and undersampling methods would lose useful emotion information. In this paper, we propose a multichannel deep architecture to improve performance in both samples and features imbalance. Specifically, we design a class correction loss function to overcome the gap between majority and minority emotions. Meanwhile, emotionspecific word embedding and a finetuning BERT are used to increase the differentiation of emotion words and sentences. Experimental results on two Chinese microblog emotion classification datasets show that our proposed architecture outperforms stateoftheart in imbalanced emotion recognition.

Deep Learning for Multimodal Emotion RecognitionAttentive Residual Disconnected RNN Human communicates using verbal and nonverbal cues. One of the most essential elements that complements the understanding of communication is emotion. Emotion is expressed not only in words, but also facial expressions, body language, tone, etc. Therefore, we formulate the emotion recognition as a multimodal task.Emotions are usually described in a sequence along with the utterances. In recent years, RNNbased models have been known to be good at modeling the entire sequence and capturing longterm dependencies. However, it lacks the ability to extract local key patterns and positioninvariant features. Hence, we adopt Deep Attentive Residual Disconnected RNN model which incorporates the concept from both RNN and CNN to enhance the ability to capture spatial and temporal features.We utilize CMU MOSEI dataset comprising of language, visual, and acoustic modalities for training and evaluating our model. The results show that Deep Attentive Residual Disconnected RNN model outperforms the baseline. Besides, the use of multimodal approach also solidifies the recognition better compared to those of single modalities.