# scb-mt-en-th-2020: A Large English-Thai Parallel Corpus

arXiv: https://arxiv.org/abs/2007.03541 · HTML: https://mlsanity.com/paper/2007.03541/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2020.

## Built on by (most cited first, 10 of 10)

- [Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning](https://mlsanity.com/paper/2402.06619/) (2024)
- [PyThaiNLP: Thai Natural Language Processing in Python](https://mlsanity.com/paper/2312.04649/) (2023)
- [Just Go Parallel: Improving the Multilingual Capabilities of Large Language Models](https://mlsanity.com/paper/2506.13044/) (2025)
- [PhayaThaiBERT: Enhancing a Pretrained Thai Language Model with Unassimilated Loanwords](https://mlsanity.com/paper/2311.12475/) (2023)
- [Representing the Under-Represented: Cultural and Core Capability Benchmarks for Developing Thai Large Language Models](https://mlsanity.com/paper/2410.04795/) (2024)
- [SEA-BED: How Do Embedding Models Represent Southeast Asian Languages?](https://mlsanity.com/paper/2508.12243/) (2025)
- [Can General-Purpose Large Language Models Generalize to English-Thai Machine Translation ?](https://mlsanity.com/paper/2410.17145/) (2024)
- [MERIT: Multilingual Expert-Reward Informed Tuning for Chinese-Centric Low-Resource Machine Translation](https://mlsanity.com/paper/2604.04839/) (2026)
- [Reference-Grounded Data Curation for Instruction-Following Thai-English Machine Translation](https://mlsanity.com/paper/2609.34770/) (2026)
- [Cross-Lingual Alignment for Decoder-Only Models using MoE Routers](https://mlsanity.com/paper/2610.01921/) (2026)

## Abstract (arXiv)

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.