Toucan: Many-to-Many Translation for 150 A frican Language Pairs (2024)

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 Afro-Lingu-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 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.

## introduction machine translation (mt) is an important technology that bridges linguistic divides and enables communication across the globe. although transfer learning methods @xcite employing multilingual language models (mlm) @xcite have benefited the field specially for languages with limited resources @xcite , a significant gap remains for many african languages. in particular, although a handful of mlms finetuned for mt for african languages @xcite @xcite ⋆ authors contributed equally. figure 1 : toucan is a powerful mt model, proficiently trained on 156 language pairs. it covers a wide spectrum of 43 african languages as well as arabic, english, and french jude @xcite , these pioneering works only serve 31 out of the 2, 000+ languages of the african continent. this lack of coverage means the issues of language barriers, the risk of language extinction, and the under-representation of diverse communities in global conversations @xcite . language barriers in particular pose significant challenges, hindering the smooth exchange of ideas, information, and cultural nuances across diverse linguistic landscapes. in contexts characterized by limited resources, where access to proficient human translators is constrained, mt has the potential to be a transformative remedy that offer unparalleled advantages in dismantling linguistic obstacles and promote heightened crosscultural comprehension. in this paper, we address this gap by presenting a family of pretrained models that we also finetune for machine translation, an extensive evaluation benchmark, and an evaluation metric with wider coverage of african languages. by introducing these, we aim to contribute to the advancement of low-resources language technology especially african languages, unlocking new possibilities for cross-cultural understanding and knowledge exchange. our main contributions can be summarized as follow: ## literature review mt has seen remarkable advancements, particularly in supporting underrepresented languages such as those spoken in africa @xcite @xcite . the development of mt tools for african languages is vital for promoting linguistic diversity, fostering cross-cultural communication, and driving socio-economic progress. in this review, we briefly cover llms supporting african languages and related topics such as datasets and evaluation tools. also, we provide additional details in section a in the appendix. ## afrolingu-mt benchmark in this section, we describe our data collection and construction procedures, along with the features of our comprehensive benchmark for evaluating african mt systems, afrolingu-mt. to create afrolingu-mt , we execute several steps, encompassing data curation, quality evaluation, pair selection, determination of translation directions, and specification of the output format. we now explain each of these steps. ## toucan models in this work, we develop a number of manyto-many afrocentric machine translation models dubbed toucan. for this purpose, we first pretrain a number of afrocentric sequence-to-sequence models that serve as the foundational backbone for our proposed machine translation toucan models. ## conclusion in this paper, we introduce a suite of resources aimed at enhancing mt for low-resource african languages. we present cheetah-1.2b and cheetah-3.7b, with 1.2b and 3.7b parameters, respectively. we further finetune these models into versatile many-to-many model capable of translating between 46 different languages, including 43 african languages and the three major non-indigenous languages in africa: arabic, english, and french. we also introduce afrolingu-mt as the largest african mt benchmark to date. we provide a comprehensive comparison between various llms by evaluating a wide range of models on our afrolingu-mt benchmark. finally we extend spbleu -spbleu 1k -a sentencepiece-based evaluation metric covering 1, 003 languages, including 614 african languages. this aims to enhance translation evaluation quality, particularly for languages historically underrepresented in translation models and benchmarks.