Few-shot Cross-lingual Aspect-Based Sentiment Analysis with Sequence-to-Sequence Models
This work addresses cross-lingual ABSA for low-resource languages, offering a practical, incremental improvement by leveraging minimal annotated data.
The paper tackled the problem of aspect-based sentiment analysis (ABSA) for low-resource languages by evaluating the impact of adding a few target language examples to training, showing that just ten examples significantly improves performance over zero-shot settings and can match monolingual baselines with 1,000 examples.
Aspect-based sentiment analysis (ABSA) has received substantial attention in English, yet challenges remain for low-resource languages due to the scarcity of labelled data. Current cross-lingual ABSA approaches often rely on external translation tools and overlook the potential benefits of incorporating a small number of target language examples into training. In this paper, we evaluate the effect of adding few-shot target language examples to the training set across four ABSA tasks, six target languages, and two sequence-to-sequence models. We show that adding as few as ten target language examples significantly improves performance over zero-shot settings and achieves a similar effect to constrained decoding in reducing prediction errors. Furthermore, we demonstrate that combining 1,000 target language examples with English data can even surpass monolingual baselines. These findings offer practical insights for improving cross-lingual ABSA in low-resource and domain-specific settings, as obtaining ten high-quality annotated examples is both feasible and highly effective.