Toucan: Many-to-Many Translation for 150 African Language Pairs
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Abstract (arXiv)
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.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 24 of its 48 references that are in the map. First posted on arXiv 5 Jul 2024.
Builds on
24 of 48 references.
References · 24
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
- 2015Neural Machine Translation of Rare Words with Subword Unitscited by 2,898
- 2018Deep contextualized word representationscited by 2,285
- 2018SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processingcited by 1,973
- 2020mT5: A massively multilingual pre-trained text-to-text transformercited by 1,813
- 2018A Call for Clarity in Reporting BLEU Scorescited by 1,790
- 2022No Language Left Behind: Scaling Human-Centered Machine Translationcited by 1,290
- 2020Multilingual Denoising Pre-training for Neural Machine Translationcited by 1,028
- 2020COMET: A Neural Framework for MT Evaluationcited by 1,024
- 2020Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillationcited by 591
- 2020Beyond English-Centric Multilingual Machine Translationcited by 559
12 more
- 2021The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translationcited by 556
- 2017Six Challenges for Neural Machine Translationcited by 334
- 2024Aya Model: An Instruction Finetuned Open-Access Multilingual Language Modelcited by 292
- 2021Quality at a Glance: An Audit of Web-Crawled Multilingual Datasetscited by 241
- 2016Transfer Learning for Low-Resource Neural Machine Translationcited by 188
- 2022A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translationcited by 97
- 2022Towards Afrocentric NLP for African Languages: Where We Are and Where We Can Gocited by 46
- 2021The Effect of Domain and Diacritics in Yor`ub'a-English Neural Machine Translationcited by 38
- 2019Massive vs. Curated Word Embeddings for Low-Resourced Languages. The Case of Yor`ub'a and Twicited by 33
- 2022TURJUMAN: A Public Toolkit for Neural Arabic Machine Translationcited by 12
- 2023Preparing the Vuk'uzenzele and ZA-gov-multilingual South African multilingual corporacited by 12
- 2021Congolese Swahili Machine Translation for Humanitarian Responsecited by 5
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Citing papers per year: 2024: 1; 2025: 5; 2026: 2.
8 papers.
- 2024Survey of Cultural Awareness in Language Models: Text and Beyondcited by 101
- 2025Charting the Landscape of African NLP: Mapping Progress and Shaping the Road Aheadcited by 15
- 2025AFRIDOC-MT: Document-level MT Corpus for African Languagescited by 11
- 2025Where Are We? Evaluating LLM Performance on African Languagescited by 9
- 2025Voice of a Continent: Mapping Africa's Speech Technology Frontiercited by 4
- 2026Skill Issue: Are Skills Language-Invariant in LLMs?cited by 1
- 2025Exploring Performance Variations in Finetuned Translators of Ultra-Low Resource Languages: Do Linguistic Differences Matter?cited by 0
- 2026AfriScience-MT: Towards Decolonizing Science in Africa through Text Translationcited by 0
- 2026Skill Issue: Are Skills Language-Invariant in LLMs?26 Aug 2026
- 2026AfriScience-MT: Towards Decolonizing Science in Africa through Text Translation28 May 2026
- 2025Exploring Performance Variations in Finetuned Translators of Ultra-Low Resource Languages: Do Linguistic Differences Matter?27 Nov 2025
- 2025Charting the Landscape of African NLP: Mapping Progress and Shaping the Road Ahead27 May 2025
- 2025Voice of a Continent: Mapping Africa's Speech Technology Frontier24 May 2025
- 2025Where Are We? Evaluating LLM Performance on African Languages26 Feb 2025
- 2025AFRIDOC-MT: Document-level MT Corpus for African Languages10 Jan 2025
- 2024Survey of Cultural Awareness in Language Models: Text and Beyond30 Oct 2024