Contrastive pre-training and instruction tuning for cross-lingual aspect-based sentiment analysis

In Natural Language Processing (NLP), aspect-based sentiment analysis (ABSA) has always been one of the critical research areas. However, due to the lack of sufficient sentiment corpora in most languages, existing research mainly focuses on English texts, resulting in limited studies on multilingual ABSA tasks. In this paper, we propose a new pre-training strategy using contrastive learning to improve the performance of cross-lingual ABSA tasks, and we construct a semantic contrastive loss to align parallel sentence representations with the same semantics in different languages. Secondly, we introduce instruction prompt template tuning, which enables the language model to fully understand the task content and learn to generate the required targets through manually constructed instruction prompt templates. During the generation process, we create a more generic placeholder template-based structured output target to capture the relationship between aspect term and sentiment polarity, facilitating cross-lingual transfer. In addition, we have introduced a copy mechanism to improve task performance further. We conduct detailed experiments and ablation analyzes on eight languages to demonstrate the importance of each of our proposed components.