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# Fixing Translation Divergences in Parallel Corpora for Neural MT

**Authors:** MinhQuang Pham, Josep Crego, Jean Senellart, [François Yvon](https://md.mova.elamur.ai/free/en/docs/papers/authors/francois-yvon/) · **Year:** 2018 · **Venue:** Proceedings of EMNLP 2018 **Link:** [aclanthology.org/D18-1328](https://aclanthology.org/D18-1328/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

[**19** citations](https://openalex.org/works?filter=cites:W2890095519) openalex · 2026-10-18[**11** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/pham-2018-fixing-translation-divergences-parallel-corpora/) of the 140673 papers whose bibliographies we read[Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing](https://aclanthology.org/volumes/D18-1/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/D18-1328/)
- [PDF](https://aclanthology.org/D18-1328.pdf)
- [DOI 10.18653/v1/d18-1328](https://doi.org/10.18653/v1/d18-1328)
- [OpenAlex](https://openalex.org/W2890095519)

**Authors (4)**

1. [MinhQuang Pham](https://openalex.org/A5101431286) [ORCID](https://orcid.org/0000-0002-9356-5611) · h 6 · 131 citations · 23 works
2. [Josep Crego](https://openalex.org/A5113263309) h 20 · 1504 citations · 150 works
3. [Jean Senellart](https://openalex.org/A5033330160) [ORCID](https://orcid.org/0000-0001-9153-5745) · h 22 · 3526 citations · 125 works
4. [François Yvon](https://openalex.org/A5030615769) [ORCID](https://orcid.org/0000-0002-7972-7442) · h 30 · 4710 citations · 534 works

Citations and author metrics from openalex (2026-10-18); authors with the same name are not always told apart.

## What it is about

Parallel corpora are noisy: some sentence pairs have extra segments, unrelated content or inaccurate translations. The authors train a neural network on the corpus itself, without labels, to score the cross-lingual similarity of words. The network yields a sentence similarity score and a word-level divergence prediction. Pairs below a score threshold are filtered out, and in some pairs the divergent segments are removed while the rest is kept, which the paper calls fixing. For English–French and English–German neural translation, training on the filtered corpus improves BLEU by +0.94 and +2.25 over the full corpus. On OpenSubtitles, fixing 2.5 million sentences adds +2.01 BLEU, while the same sentences added unchanged added nothing. The method needs no supervision, and the code is public.

## Ideas

- I1. A count-based divergence score for verse pairs: for each word pair take the alignment probability from the IBM Model 1 table in `ibm.rs`, and mark a verse divergent when its aligned mass is low in both directions. This replaces the neural similarity with a deterministic rule that can be explained word by word.
- I2. Filter, then measure: remove the divergent verses before the standard encoder is learned, and compare exact-match and residual bits. Run each twice to check determinism. A gain would come from a cleaner standard, not from an author delta, so report them separately.
- I3. Partial fix for a verse with one divergent clause: keep the clause whose meta tokens agree with the other translations of the same verse, and count how many verses this rescues.

## For Mova

The code is `tools/translate/ibm.rs` and `measure.rs`. The divergence filter touches open problem 6 (weight the corpora one vote each), because the Bible pairs are the main data for the standard. The neural parts of the paper are not for Mova; I1 and I2 are the count-based forms of the same idea.

Topics: [Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/topics/machine-translation/)

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.18653/v1/d18-1328](https://doi.org/10.18653/v1/d18-1328)

```
@inproceedings{pham2018fixing,
  title = {{Fixing Translation Divergences in Parallel Corpora for Neural MT}},
  author = {Pham, MinhQuang and Crego, Josep and Senellart, Jean and Yvon, François},
  booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing},
  year = {2018},
  doi = {10.18653/v1/d18-1328},
  url = {https://doi.org/10.18653/v1/d18-1328}
}
```

- [← previous Normalisation of Historical Text Using Context-Sensitive Weighted Levenshtein Distance and Compound Splitting](https://md.mova.elamur.ai/free/en/docs/papers/pettersson-2013-normalisation-historical-text-context-sensitive/)
- [next → Apertium-fin-eng–Rule-based Shallow Machine Translation for WMT 2019 Shared Task](https://md.mova.elamur.ai/free/en/docs/papers/pirinen-2019-apertium-fin-eng-rule-based/)
- [↑ back to Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/mtrans/)

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