Toucan: Many-to-Many Translation for 150 African Language Pairs

TL;DR

This paper introduces Toucan, a many-to-many translation model supporting 156 African language pairs, along with new resources and benchmarks to improve NLP translation capabilities for low-resource African languages.

Contribution

The paper presents Toucan, a novel Afrocentric translation model for numerous African languages, and develops AfroLingu-MT benchmark and spBLEU-1K evaluation metric.

Findings

Toucan outperforms existing models on African language translation tasks.

Development of AfroLingu-MT benchmark for African languages.

Introduction of spBLEU-1K metric for multilingual translation evaluation.

Abstract

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.