弥合语言差距：增强少样本语言适应
Bridging Language Gaps: Enhancing Few-Shot Language Adaptation
AI总结：
本文提出CoLAP方法，通过对比学习与跨语言表示相结合，在少样本条件下实现高资源到低资源语言的高效知识迁移，并在自然语言推理和关系抽取任务上超越现有基线，显著缩小跨语言性能差距。
摘要：
语言资源的不均衡给多语言自然语言处理带来了挑战：高资源语言受益于海量数据，而低资源语言缺乏足够数据进行有效训练。我们提出的基于提示的对比语言对齐（Contrastive Language Alignment with Prompting，CoLAP）方法通过将对比学习与跨语言表示相结合来应对这一差距，促进任务特定知识从高资源语言向低资源语言的迁移。该方法的主要优势在于其数据效率，能够快速适应新语言，并减少对大规模标注数据集的需求。我们使用仅编码器和仅解码器的多语言语言模型，在自然语言理解任务（包括自然语言推理和关系抽取）上开展实验，评估其在高资源与低资源语言上的性能。结果表明，即使在可用数据有限的情况下，CoLAP 仍优于少样本跨语言迁移基线和上下文学习方法。这有效缩小了跨语言性能差距，有助于开发更高效的多语言自然语言处理技术。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
