构建多语言数据集CUTE，提升低资源语言的跨语言迁移能力
CUTE: A Multilingual Dataset for Enhancing Cross-Lingual Knowledge Transfer in Low-Resource Languages
- 用机器翻译构建中、英、维、藏四语语料库，含平行与非平行数据
- 数据集达50GB，是目前维吾尔语和藏语最大的开源语料库
- 验证了机器翻译质量接近中英水平，适合低资源语言研究
大型语言模型在多种自然语言处理任务中表现出卓越的零样本能力，显著提升用户体验与效率。然而，这一优势主要局限于资源丰富语言。对于众多低资源语言，支持仍显不足，训练语料匮乏被认为是主要原因。本文构建并开源了CUTE中文、维吾尔语、藏语、英语数据集，包含两套各25GB的四语言语料（一套平行、一套非平行），均通过机器翻译获得。CUTE涵盖两种资源丰富语言（中文、英文）和两种低资源语言（维吾尔语、藏语）。在构建前，人工评估表明中文-维吾尔语及中文-藏语的机器翻译质量已接近中文-英文水平。CUTE是迄今最大规模的维吾尔语与藏语开源语料库，实证其能有效增强大模型对低资源语言的处理能力，并探究了语料平行性在跨语言迁移学习中的作用。相关语料与模型已公开共享。
原文摘要 · Abstract (English)
Large Language Models (LLMs) demonstrate exceptional zero-shot capabilities in various NLP tasks, significantly enhancing user experience and efficiency. However, this advantage is primarily limited to resource-rich languages. For the diverse array of low-resource languages, support remains inadequate, with the scarcity of training corpora considered the primary cause. We construct and open-source CUTE Chinese, Uyghur, Tibetan,English dataset, consisting of two 25GB sets of four-language corpora (one parallel and one non-parallel), obtained through machine translation. CUTE encompasses two resource-rich languages (Chinese and English) and two low-resource languages (Uyghur and Tibetan). Prior to constructing CUTE, human assessment validates that the machine translation quality between Chinese-Uyghur and Chinese-Tibetan approaches that of Chinese-English translation. CUTE represents the largest open-source corpus for Uyghur and Tibetan languages to date, and we demonstrate its effectiveness in enhancing LLMs' ability to process low-resource languages while investigating the role of corpus parallelism in cross-lingual transfer learning. The CUTE corpus and related models are made publicly available to the research community.
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