A Sentiment Similarity-Oriented Attention Model with Multi-task Learning for Text-Based Emotion Recognition

摘要

Emotion recognition based on text modality has been one of the major topics in the field of emotion recognition in conversation. How to extract efficient emotional features is still a challenge. Previous studies utilize contextual semantics and emotion lexicon for affect modeling. However, they ignore information that may be conveyed by the emotion labels themselves. To address this problem, we propose the sentiment similarity-oriented attention (SSOA) mechanism, which uses the semantics of emotion labels to guide the model's attention when encoding the input conversations. Thus to extract emotion-related information from sentences. Then we use the convolutional neural network (CNN) to extract complex informative features. In addition, as discrete emotions are highly related with the Valence, Arousal, and Dominance (VAD) in psychophysiology, we train the VAD regression and emotion classification tasks together by using multi-task learning to extract more robust features. The proposed method outperforms the benchmarks by an absolute increase of over 3.65% in terms of the average F1 for the emotion classification task, and also outperforms previous strategies for the VAD regression task on the IEMOCAP database.