The primary objective of our work is to build a large-scale English-Thai dataset for machine translation. We construct an English-Thai machine translation dataset with over 1 million segment pairs, curated from various sources, namely news, Wikipedia articles, SMS messages, task-based dialogs, web-crawled data and government documents. Methodology for gathering data, building parallel texts and removing noisy sentence pairs are presented in a reproducible manner. We train machine translation models based on this dataset. Our models' performance are comparable to that of Google Translation API (as of May 2020) for Thai-English and outperform Google when the Open Parallel Corpus (OPUS) is included in the training data for both Thai-English and English-Thai translation. The dataset, pre-trained models, and source code to reproduce our work are available for public use.
@article{arxiv.2007.03541,
title = {scb-mt-en-th-2020: A Large English-Thai Parallel Corpus},
author = {Lalita Lowphansirikul and Charin Polpanumas and Attapol T. Rutherford and Sarana Nutanong},
journal= {arXiv preprint arXiv:2007.03541},
year = {2021}
}