缅甸XNLI：构建数据集并探索缅甸语自然语言推理的低资源方法
Myanmar XNLI: Building a Dataset and Exploring Low-resource Approaches to Natural Language Inference with Myanmar
AI总结：
本文针对低资源语言缅甸语扩展XNLI任务，构建myXNLI数据集，评估多语言模型并探索数据增强方法（准确率提升2个百分点），还研究方法对其他低资源语言的泛化性，助力低资源语言NLP研究。
摘要：
尽管自然语言处理（NLP）领域近期取得显著进展，但将大型语言模型（LLM）应用于低资源语言仍是重大挑战。这一点在跨语言自然语言推理（XNLI）等基准测试中可见一斑，XNLI是一项关键任务，可展示NLP系统在15种语言中的跨语言能力。本文将XNLI任务扩展至另一种低资源语言——缅甸语，以此作为更广泛低资源语言的代理挑战，并做出三项核心贡献。首先，我们使用社区众包方法构建了名为Myanmar XNLI（myXNLI）的数据集，作为现有XNLI语料库的扩展。这涉及社区构建后专家验证的两阶段流程；通过分析，我们证明并量化了专家验证阶段在低资源语言社区构建语境中的价值。我们将myXNLI数据集提供给社区以供未来研究。其次，我们在myXNLI基准上评估了近期的多语言模型，并探索了数据增强方法以提升模型性能。我们的数据增强方法使缅甸语的模型准确率提高了多达2个百分点，同时也提升了其他语言的性能。第三，我们研究了这些数据增强方法在XNLI数据集中其他低资源语言上的泛化能力。
英文摘要：
Despite dramatic recent progress in NLP, it is still a major challenge to apply Large Language Models (LLM) to low-resource languages. This is made visible in benchmarks such as Cross-Lingual Natural Language Inference (XNLI), a key task that demonstrates cross-lingual capabilities of NLP systems across a set of 15 languages. In this paper, we extend the XNLI task for one additional low-resource language, Myanmar, as a proxy challenge for broader low-resource languages, and make three core contributions. First, we build a dataset called Myanmar XNLI (myXNLI) using community crowd-sourced methods, as an extension to the existing XNLI corpus. This involves a two-stage process of community-based construction followed by expert verification; through an analysis, we demonstrate and quantify the value of the expert verification stage in the context of community-based construction for low-resource languages. We make the myXNLI dataset available to the community for future research. Second, we carry out evaluations of recent multilingual language models on the myXNLI benchmark, as well as explore data-augmentation methods to improve model performance. Our data-augmentation methods improve model accuracy by up to 2 percentage points for Myanmar, while uplifting other languages at the same time. Third, we investigate how well these data-augmentation methods generalise to other low-resource languages in the XNLI dataset.
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。
