You download our OpenNMT-tf (TensorFlow) Arabic-to-English model here. The related SentencePiece models can be downloaded here. The config file is here.
Also, our Arabic-to-English model can be downloaded here. The config is here.
If you want to fine-tune the model, here are the instructions to continue training OpenNMT-tf models.
I hope this helps. If you have questions, please let me know.
If you use the model, please cite our paper, which explains the process of building the model:
title = "Domain-Specific Text Generation for Machine Translation",
author = "Moslem, Yasmin and
Haque, Rejwanul and
Kelleher, John and
booktitle = "Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)",
month = sep,
year = "2022",
address = "Orlando, USA",
publisher = "Association for Machine Translation in the Americas",
url = "https://aclanthology.org/2022.amta-research.2",
pages = "14--30",
abstract = "Preservation of domain knowledge from the source to target is crucial in any translation workflow. It is common in the translation industry to receive highly-specialized projects, where there is hardly any parallel in-domain data. In such scenarios where there is insufficient in-domain data to fine-tune Machine Translation (MT) models, producing translations that are consistent with the relevant context is challenging. In this work, we propose leveraging state-of-the-art pretrained language models (LMs) for domain-specific data augmentation for MT, simulating the domain characteristics of either (a) a small bilingual dataset, or (b) the monolingual source text to be translated. Combining this idea with back-translation, we can generate huge amounts of synthetic bilingual in-domain data for both use cases. For our investigation, we used the state-of-the-art MT architecture, Transformer. We employed mixed fine-tuning to train models that significantly improve translation of in-domain texts. More specifically, our proposed methods achieved improvements of approximately 5-6 BLEU and 2-3 BLEU, respectively, on Arabic-to-English and English-to-Arabic language pairs. Furthermore, the outcome of human evaluation corroborates the automatic evaluation results.",