# A Large Parallel Corpus of Full-Text Scientific Articles

arXiv: https://arxiv.org/abs/1905.01852 · HTML: https://mlsanity.com/paper/1905.01852/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2019.

## Built on by (most cited first, 12 of 12)

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- [Language Modelling Approaches to Adaptive Machine Translation](https://mlsanity.com/paper/2401.14559/) (2024)
- [Domain-Specific Translation with Open-Source Large Language Models: Resource-Oriented Analysis](https://mlsanity.com/paper/2412.05862/) (2024)
- [SMAuC -- The Scientific Multi-Authorship Corpus](https://mlsanity.com/paper/2211.02477/) (2022)
- [ACADATA: Parallel Dataset of Academic Data for Machine Translation](https://mlsanity.com/paper/2510.12621/) (2025)
- [Enhancing Scientific Discourse: Machine Translation for the Scientific Domain](https://mlsanity.com/paper/2605.20912/) (2026)

## Abstract (arXiv)

The Scielo database is an important source of scientific information in Latin America, containing articles from several research domains. A striking characteristic of Scielo is that many of its full-text contents are presented in more than one language, thus being a potential source of parallel corpora. In this article, we present the development of a parallel corpus from Scielo in three languages: English, Portuguese, and Spanish. Sentences were automatically aligned using the Hunalign algorithm for all language pairs, and for a subset of trilingual articles also. We demonstrate the capabilities of our corpus by training a Statistical Machine Translation system (Moses) for each language pair, which outperformed related works on scientific articles. Sentence alignment was also manually evaluated, presenting an average of 98.8% correctly aligned sentences across all languages. Our parallel corpus is freely available in the TMX format, with complementary information regarding article metadata.