mlsanityData to 7 Oct 2026

OpusCleaner and OpusTrainer, open source toolkits for training Machine Translation and Large language models

arXiv 2311.14838 ↗First posted 24 Nov 2023cs.CLcited by 1

Not on the map. Why?

Abstract (arXiv)

Developing high quality machine translation systems is a labour intensive, challenging and confusing process for newcomers to the field. We present a pair of tools OpusCleaner and OpusTrainer that aim to simplify the process, reduce the amount of work and lower the entry barrier for newcomers. OpusCleaner is a data downloading, cleaning, and proprocessing toolkit. It is designed to allow researchers to quickly download, visualise and preprocess bilingual (or monolingual) data that comes from many different sources, each of them with different quality, issues, and unique filtering/preprocessing requirements. OpusTrainer is a data scheduling and data augmenting tool aimed at building large scale, robust machine translation systems and large language models. It features deterministic data mixing from many different sources, on-the-fly data augmentation and more. Using these tools, we showcase how we can use it to create high quality machine translation model robust to noisy user input; multilingual models and terminology aware models.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 7 of its 9 references that are in the map. First posted on arXiv 24 Nov 2023.

Builds on

7 of 9 references.

References · 7

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processingcited by 1,973
  3. 2015Improving Neural Machine Translation Models with Monolingual Datacited by 784
  4. 2018Marian: Fast Neural Machine Translation in C++cited by 202
  5. 2021Many-to-English Machine Translation Tools, Data, and Pretrained Modelscited by 31
  6. 2020Complete Multilingual Neural Machine Translationcited by 30
  7. 2023Terminology-Aware Translation with Constrained Decoding and Large Language Model Promptingcited by 8

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.