Enhancing Scientific Discourse: Machine Translation for the Scientific Domain

Dimitris Roussis, Sokratis Sofianopoulos, Stelios Piperidis
5/20/2026
cs.CL

No confident code implementation found for this paper.

Abstract

The increasing volume of scientific research necessitates effective communication across language barriers. Machine translation (MT) offers a promising solution for accessing international publications. However, the scientific domain presents unique challenges due to its specialized vocabulary and complex sentence structures. In this paper, we present the development of a collection of parallel and monolingual corpora for the scientific domain. The corpora target the language pairs Spanish-English, French-English, and Portuguese-English. For each language pair, we create a large general scientific corpus as well as four smaller corpora focused on the domains of: Cancer Research, Energy Research, Neuroscience, and Transportation research. To evaluate the quality of these corpora, we utilize them for fine-tuning general-purpose neural machine translation (NMT) systems. We provide details regarding the corpus creation process, the fine-tuning strategies employed, and we conclude with the evaluation results.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Building a Parallel Corpus and Training Translation Models Between Luganda and English

87% match2023

Machine Translation for Nko: Tools, Corpora and Baseline Results

87% match1 repo2023

First Attempt at Building Parallel Corpora for Machine Translation of Northeast India's Very Low-Resource Languages

86% match2023

The eBible Corpus: Data and Model Benchmarks for Bible Translation for Low-Resource Languages

86% match1 repo62023

BhashaSetu: A Data-Centric Approach to Low-Resource Machine Translation

86% match2026

Developing an English-Efik Corpus and Machine Translation System for Digitization Inclusion

85% match2026

Cite this paper

@article{roussis2026enhancing,
  title  = {Enhancing Scientific Discourse: Machine Translation for the Scientific Domain},
  author = {Dimitris Roussis and Sokratis Sofianopoulos and Stelios Piperidis},
  year   = {2026},
  eprint = {2605.20912},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2605.20912v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2605.20912.svg)](https://codeofpaper.com/paper/2605.20912)

Discussion