Toucan：面向150个非洲语言对的多对多翻译
Toucan: Many-to-Many Translation for 150 African Language Pairs
AI总结：
本研究针对非洲低资源语言机器翻译空白，推出两款Cheetah语言模型，微调得到支持156个非洲语言对的Toucan翻译模型，构建AfroLingu-MT基准验证其性能优势，还训练了覆盖千种语言的spBLEU-1K优化评测指标，推动非洲等低资源地区NLP发展与文化交流。
摘要：
我们针对自然语言处理（NLP）领域的一个显著空白，推出了一系列资源，旨在改善低资源语言的机器翻译（MT）效果，重点关注非洲语言。首先，我们推出了两个语言模型（LM）：Cheetah-1.2B和Cheetah-3.7B，参数量分别为12亿和37亿。接下来，我们对上述模型进行微调，打造出toucan——一款以非洲语言为核心的机器翻译模型，支持156个非洲语言对。为评估Toucan的性能，我们精心构建了一个全面的机器翻译基准，名为AfroLingu-MT，专门用于机器翻译评测。Toucan的表现显著优于其他模型，展现出其在非洲语言机器翻译任务上的卓越性能。最后，我们训练了一个新模型spBLEU-1K，用于优化翻译评估指标，该模型覆盖1000种语言，其中包括614种非洲语言。本研究旨在推动NLP领域发展，促进跨文化理解与知识交流，尤其是在非洲等语言资源有限的地区。Toucan项目的GitHub仓库地址为：https://github.com/UBC-NLP/Toucan。
英文摘要：
We address a notable gap in Natural Language Processing (NLP) by introducing a collection of resources designed to improve Machine Translation (MT) for low-resource languages, with a specific focus on African languages. First, we introduce two language models (LMs), Cheetah-1.2B and Cheetah-3.7B, with 1.2 billion and 3.7 billion parameters respectively. Next, we finetune the aforementioned models to create toucan, an Afrocentric machine translation model designed to support 156 African language pairs. To evaluate Toucan, we carefully develop an extensive machine translation benchmark, dubbed AfroLingu-MT, tailored for evaluating machine translation. Toucan significantly outperforms other models, showcasing its remarkable performance on MT for African languages. Finally, we train a new model, spBLEU-1K, to enhance translation evaluation metrics, covering 1K languages, including 614 African languages. This work aims to advance the field of NLP, fostering cross-cultural understanding and knowledge exchange, particularly in regions with limited language resources such as Africa. The GitHub repository for the Toucan project is available at https://github.com/UBC-NLP/Toucan.
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。
