Cross-Lingual Emotion Classification with Auxiliary and Attention Neural Networks.

In the literature, various supervised learning approaches have been adopted to address the task of emotion classification. However, the performance of these approaches greatly suffers when the size of the labeled data is limited. In this paper, we tackle this challenge from a cross-lingual sensoria where the labeled data in a resource-rich language (i.e., English in this study) is employed to improve the emotion classification performance in a resource-poor language (i.e., Chinese in this study). Specifically, we first use machine translation services to eliminate the language gap between Chinese and English data and then propose a joint learning framework to leverage both Chinese and English data, which develops auxiliary representations from several auxiliary emotion classification tasks. Furthermore, in our joint learning approach, we introduce an attention mechanism to capture informative words. Empirical studies demonstrate the effectiveness of the proposed approach to emotion classification.