Abstract
The disparity in language resources poses a challenge in multilingual NLP, with high-resource languages benefiting from extensive data, while low-resource languages lack sufficient data for effective training. Our Contrastive Language Alignment with Prompting (CoLAP) method addresses this gap by integrating contrastive learning with cross-lingual representations, facilitating task-specific knowledge transfer from high-resource to lower-resource languages. The primary advantage of our approach is its data efficiency, enabling rapid adaptation to new languages and reducing the need for large labeled datasets. We conduct experiments with multilingual encoder-only and decoder-only language models on natural language understanding tasks, including natural language inference and relation extraction, evaluating performance across both high- and low-resource languages. Our results demonstrate that CoLAP outperforms few-shot cross-lingual transfer baselines and in-context learning, even with limited available data. This effectively narrows the cross-lingual performance gap, contributing to the development of more efficient multilingual NLP techniques.
Abstract (translated)
语言资源的不均衡是多语种自然语言处理（NLP）面临的一大挑战，高资源语言得益于丰富的数据支持，而低资源语言则因缺乏足够有效的训练数据而处于不利地位。我们提出了一种对比语言对齐与提示方法（Contrastive Language Alignment with Prompting, CoLAP），通过将对比学习与跨语言表示相结合，实现了从高资源语言向低资源语言的特定任务知识转移。我们的方法的主要优势在于其高效的数据利用性，能够快速适应新语言，并且减少了大型标注数据集的需求。 我们在多语种编码器和解码器独立的语言模型上进行了实验，涉及自然语言理解任务（如自然语言推理和关系抽取），并且在高资源与低资源语言的性能上都进行了评估。我们的结果表明，即使是在有限的数据可用性条件下，CoLAP也优于少量样本跨语言迁移基准方法以及上下文学习方法，在一定程度上缩小了跨语种表现差距，并为更有效的多语种NLP技术的发展做出了贡献。
URL
https://arxiv.org/abs/2508.19464
