---
title: "Bifixer and Bicleaner — Ramírez-Sánchez 2020"
description: "Neural MT is more sensitive to noisy training data than older statistical systems."
authors: ["Gema Ramírez-Sánchez", "Jaume Zaragoza-Bernabeu", "Marta Bañón", "Sergio Ortiz Rojas"]
year: 2020
venue: "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation"
source: "https://aclanthology.org/2020.eamt-1.31/"
licence: "CC BY-ND 4.0 (EAMT)"
licence_url: "https://creativecommons.org/licenses/by-nd/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/papers/sanchez-2020-bifixer-bicleaner-two-open-source/"
---
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# Bifixer and Bicleaner: two open-source tools to clean your parallel data

**Authors:** [Gema Ramírez-Sánchez](https://md.research.elamur.ai/free/authors/gema-ramirez-sanchez/), Jaume Zaragoza-Bernabeu, [Marta Bañón](https://md.research.elamur.ai/free/authors/marta-banon/), [Sergio Ortiz Rojas](https://md.research.elamur.ai/free/authors/sergio-ortiz-rojas/) · **Year:** 2020 · **Venue:** Proceedings of the 22nd Annual Conference of the European Association for Machine Translation · **Licence:** [CC BY-ND 4.0 (EAMT)](https://creativecommons.org/licenses/by-nd/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2020.eamt-1.31/) · [PDF](https://aclanthology.org/2020.eamt-1.31.pdf)

**Citations:** [54](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary

Neural MT is more sensitive to noisy training data than older statistical systems. Yet widely used public parallel corpora are often fed to training without any cleaning. The authors test two open-source tools from the ParaCrawl project on four English–Portuguese corpora: Europarl, OpenSubtitles, JW300 and WikiMatrix. The tools were originally built for raw web-crawled text.

- **Bifixer** repairs data before filtering. It fixes encoding and Mojibake, HTML entities and common typos, re-splits glued sentences, and marks exact and near duplicates.
- **Bicleaner** then filters the data. It applies 37 hand-written rules and scores each remaining sentence pair with a random-forest classifier. To keep the evaluation fair, the authors trained new classifiers that excluded the tested corpora.

The authors trained Transformer systems in both translation directions, before and after cleaning, using two classifier thresholds (0.5 and 0.7). Cleaning shrank the noisier corpora sharply and shortened training time. BLEU was equal or slightly higher in every setting, and the strictest filtering gave the best scores. This shows that cleaning even well-known corpora is a cheap step that can save disk space and compute without hurting translation quality.

## Key points

- **Corpus sizes (EN–PT):** Europarl v7 has 2.2M sentence pairs, OpenSubtitles 2018 has over 33M, JW300 has 2.1M and WikiMatrix has 4.4M.
- **Bifixer results (averages):** re-splitting recovers about 1.1% new sentences, about 4.6% of sentences get fixed, and about 9.8% are removed as duplicates or near duplicates. OpenSubtitles alone loses 8.1M pairs (24.5%) at this stage.
- **Manual check of 100 sample pairs per corpus:** problems appeared in 30 pairs from WikiMatrix, 11 from OpenSubtitles and 2 from Europarl.
- **MT setup:** 24 Transformer-base systems built with Marian and SentencePiece (32k vocabulary). They were evaluated on TED Talks dev/test sets with sacreBLEU.
- **Main result:** the largest gain, +1 to +2.2 BLEU, came from OpenSubtitles after about 22M pairs (two-thirds of the corpus) were removed. WikiMatrix shrank by about half. Europarl EN→PT stayed unchanged, and the authors suggest some rules may be too strict for already clean data.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/shelves/linguistics/) · [Machine translation](https://md.research.elamur.ai/free/shelves/machine-translation/)

## Cited by

Cited by

54 papers of our library cite it (of the 140673 whose bibliographies we read); 4 of them have a page here, 50 not yet.

- [Tower: An Open Multilingual Large Language Model for Translation-Related Tasks](https://md.research.elamur.ai/free/papers/alves-2024-tower-open-multilingual-large-language/)
- [EuroLLM: Multilingual Language Models for Europe](https://md.research.elamur.ai/free/papers/martins-2024-eurollm-multilingual-language-models-europe/)
- [ParaCLEAN: Improving Translation Quality through Systematic Parallel Data Cleaning](https://md.research.elamur.ai/free/papers/mash-2026-paraclean-improving-translation-quality-through/)
- [When Translations Surprise: Human Awareness of Predictability in Translations](https://md.research.elamur.ai/free/papers/romero-2026-when-translations-surprise-human-awareness/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{ramirezsanchez2020bifixer,
  title = {{Bifixer and Bicleaner: two open-source tools to clean your parallel data}},
  author = {Ramírez-Sánchez, Gema and Zaragoza-Bernabeu, Jaume and Bañón, Marta and Rojas, Sergio Ortiz},
  booktitle = {Proceedings of the 22nd Annual Conference of the European Association for Machine Translation},
  year = {2020},
  url = {https://aclanthology.org/2020.eamt-1.31/}
}
```

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## References

15 references, 10 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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