Few-shot Cross-lingual Aspect-Based Sentiment Analysis with Sequence-to-Sequence Models

2025-08-11

AI Summary

Low-resource languages suffer from limited labeled data, hindering cross-lingual aspect-based sentiment analysis (ABSA); existing approaches often rely on external translation and overlook the potential of few-shot learning in the target language. This paper proposes a lightweight few-shot transfer framework that jointly trains a sequence-to-sequence model on English data and a minimal number (10–1,000) of labeled target-language samples. Experiments across four ABSA subtasks and six low-resource languages demonstrate that just 10 target-language examples significantly outperform zero-shot baselines, while 1,000 examples surpass fully supervised monolingual models—matching the performance of sophisticated constrained decoding methods. Crucially, the approach requires no external translation tools and incurs no additional inference overhead. It establishes an efficient, general-purpose, and easily deployable paradigm for low-resource ABSA.

Technology Category

• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Computer Vision: Language and Vision

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• Addressing low-resource languages in aspect-based sentiment analysis
• Improving cross-lingual ABSA with few-shot target language examples
• Evaluating sequence-to-sequence models for multilingual sentiment tasks

Innovation

• Few-shot target language examples enhance training
• Sequence-to-sequence models improve cross-lingual ABSA
• Combining target and English data surpasses monolingual baselines