A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translation
This repository contains a newly created MT dataset in the news domain known as MAFAND-MT for 16 languages and 5 existing news MT corpus. We also provide the code for training MT models using pre-trained models like MT5, MBART, ByT5 and M2M-100, and a notebook that can be used on Google Colab. For you to use the code, your dataset should be in json format and you have to specify the right language code, if the language code is not supported by the pre-trained model, you can use a fake language code supported.
The code is based on HuggingFace implementation (License: Apache 2.0).
The license of the MT dataset is in CC-BY-4.0-NC, the French/English source texts we translated from have difference licenses depending on the news website license.
- python
- transformers : state-of-the-art Natural Language Processing for TensorFlow 2.0 and PyTorch.
- sacrebleu : for BLEU, ChrF evaluation
- Other requirements are listed here
pip install transformers accelerate datasets sentencepiece protobuf sacrebleu py7zr torchThe dataset includes the following languages:
- Amharic (amh)
- Bambara (bam)
- Ghomala (bbj)
- Ewe (ewe)
- Fon (fon)
- Hausa (hau): we only created lines 1 - 2767 in the train set. Others are from Khamenei
- Kinyarwanda (kin): only dev and test sets
- Luganda (lug)
- Luo (luo): currently not available due to copyright issues.
- Mossi (mos)
- Nigerian-Pidgin (pcm)
- Chichewa (nya): only dev and test sets
- Shona (sna): only dev and test sets
- Swahili (swa): dev/test set was created, train set was obtained from Global Voices on OPUS
- Setswana (tsn)
- Twi (twi)
- Wolof (wol)
- Xhosa (xho) --- only dev and test sets
If you use existing corpus, please cite their papers
- Igbo (ibo): We make use of Igbo News MT corpus, we defined new train/dev/test
- Yoruba (yor): from the news domain part of MENYO-20k, we defined new modified train/dev/test split
- Zulu (zul): obtained from Umsuka on Zenodo. We use the test split, but defined a new dev split
For the adaptation of mT5 and ByT5 models, we trained on 17 African languages and 3 high-resource languages (English, French and Arabic). The dataset is available on Zenodo i.e AfroMAFT Corpus. Thank you Jesujoba Alabi for preparing the data.
The pre-training code is available in mt5_byt5_pre-training directory. A big thank you to Xiaoyu Shen for providing the code and instructions. The pre-trained AfriMT5 and AfriByT5 are available on HuggingFace Model Hub
Similar to mT5, we performed continued pre-trained on AfroMAFT Corpus. The instructions are provided in mbart_pre-training. A big thank you to Machel Reid and Ernie Chang. The AfriMBART model is also available on HuggingFace Model Hub
Fine-tune mT5, M2M100, and byT5 using HuggingFace Transformers
Step 0: Preprocess your text to be in json lines
Step 1: Install the necessary modules in requirments.txt
Step 2: Fine-tune the pre-trained model, you may use the base model e.g "google/byt5-base" and "google/mt5-base"
python run_translation.py \
--model_name_or_path google/byt5-base \
--do_train \
--do_eval \
--source_lang en \
--target_lang yo \
--source_prefix "translate English to Yoruba: " \
--train_file data/en_yo/train.json \
--validation_file data/en_yo/dev.json \
--test_file data/en_yo/test.json \
--output_dir byt5_en_yo \
--max_source_length 200 \
--max_target_length 200 \
--per_device_train_batch_size=10 \
--per_device_eval_batch_size=10 \
--overwrite_output_dir \
--predict_with_generate \
--save_steps 50000 \
--num_beams 10 \
--do_predict
Testing the model:
python run_translation.py \
--model_name_or_path byt5_en_yo \
--source_lang en \
--target_lang yo \
--source_prefix "translate English to Yoruba: " \
--train_file data/en_yo/train.json \
--validation_file data/en_yo/dev.json \
--test_file data/en_yo/test.json \
--output_dir byt5_en_yo \
--max_source_length 200 \
--max_target_length 200 \
--per_device_train_batch_size=10 \
--per_device_eval_batch_size=10 \
--overwrite_output_dir \
--predict_with_generate \
--save_steps 50000 \
--num_beams 10 \
--do_predict
For MBART and M2M100 e.g "facebook/m2m100_418M", you need to specify the language to generate using "forced_bos_token"
python run_translation.py \
--model_name_or_path facebook/m2m100_418M \
--do_train \
--do_eval \
--train_file data/en_yo/train.json \
--validation_file data/en_yo/dev.json \
--test_file data/en_yo/test.json \
--source_lang en \
--target_lang yo \
--output_dir m2m100_en_yo \
--per_device_train_batch_size=4 \
--per_device_eval_batch_size=4 \
--overwrite_output_dir \
--predict_with_generate \
--forced_bos_token yo \
--save_steps 50000 \
--num_beams 10 \
--do_predict
@inproceedings{adelani-etal-2022-thousand,
title = "A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for {A}frican News Translation",
author = "Adelani, David and
Alabi, Jesujoba and
Fan, Angela and
Kreutzer, Julia and
Shen, Xiaoyu and
Reid, Machel and
Ruiter, Dana and
Klakow, Dietrich and
Nabende, Peter and
Chang, Ernie and
Gwadabe, Tajuddeen and
Sackey, Freshia and
Dossou, Bonaventure F. P. and
Emezue, Chris and
Leong, Colin and
Beukman, Michael and
Muhammad, Shamsuddeen and
Jarso, Guyo and
Yousuf, Oreen and
Niyongabo Rubungo, Andre and
Hacheme, Gilles and
Wairagala, Eric Peter and
Nasir, Muhammad Umair and
Ajibade, Benjamin and
Ajayi, Tunde and
Gitau, Yvonne and
Abbott, Jade and
Ahmed, Mohamed and
Ochieng, Millicent and
Aremu, Anuoluwapo and
Ogayo, Perez and
Mukiibi, Jonathan and
Ouoba Kabore, Fatoumata and
Kalipe, Godson and
Mbaye, Derguene and
Tapo, Allahsera Auguste and
Memdjokam Koagne, Victoire and
Munkoh-Buabeng, Edwin and
Wagner, Valencia and
Abdulmumin, Idris and
Awokoya, Ayodele and
Buzaaba, Happy and
Sibanda, Blessing and
Bukula, Andiswa and
Manthalu, Sam",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.223",
doi = "10.18653/v1/2022.naacl-main.223",
pages = "3053--3070",
abstract = "Recent advances in the pre-training for language models leverage large-scale datasets to create multilingual models. However, low-resource languages are mostly left out in these datasets. This is primarily because many widely spoken languages that are not well represented on the web and therefore excluded from the large-scale crawls for datasets. Furthermore, downstream users of these models are restricted to the selection of languages originally chosen for pre-training. This work investigates how to optimally leverage existing pre-trained models to create low-resource translation systems for 16 African languages. We focus on two questions: 1) How can pre-trained models be used for languages not included in the initial pretraining? and 2) How can the resulting translation models effectively transfer to new domains? To answer these questions, we create a novel African news corpus covering 16 languages, of which eight languages are not part of any existing evaluation dataset. We demonstrate that the most effective strategy for transferring both additional languages and additional domains is to leverage small quantities of high-quality translation data to fine-tune large pre-trained models.",
}