Improving Cross-lingual Aspect-based Sentiment Analysis with Sememe Bridge

This is the authors' abstract. We don't add key points for this paper.

Aspect-based Sentiment Analysis (ABSA) comprises numerous subtasks including aspect term extraction (AE), opinion term extraction (OE), opinion pair extraction (PE), and triplet extraction (TE). Current research in Chinese ABSA primarily concentrates on aspect terms and sentiment polarity, with insufficient emphasis on opinion terms. This article aims to provide a viable solution for the unannotated Chinese ABSA subtasks such as OE, PE, and TE. First, we develop an English-Chinese parallel dataset for ABSA using a semi-automatic process involving machine translation and a word aligner. Second, we examine the efficacy of cross-lingual transfer methods. Third, we propose a plug-and-play transfer method based on sememe knowledge. Sememes are the language-independent smallest semantic units that encapsulate the components, commonalities, and attributes of things extracted from the real world, which can bridge the gap between English and Chinese. Experimental results show that our proposed method brings significant improvements for Chinese ABSA, and achieves a maximum increase of 8% on the F1 metric for model transfer and label transfer on OE, PE and TE.