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Neural Fuzzy Repair: Integrating Fuzzy Matches into Neural Machine Translation

Paper · 2019 · Bram Bulte and Arda Tezcan

Summary

Translation memories (TMs) stay valuable in professional translation because close matches from earlier work often need little editing. In typical CAT workflows, however, NMT only serves as a fallback when the TM offers no good match. Bulté and Tezcan propose Neural Fuzzy Repair (NFR), which brings TM knowledge into NMT purely through data preprocessing. For each sentence, the TM is searched for comparable source-side entries using token-level edit distance, sped up by SetSimilaritySearch candidate filtering. The target sides of the best matches are then appended to the source, separated by a break token. A standard OpenNMT model is trained on this data in one of two ways: as a dedicated system paired with a backoff NMT model, or as a single unified system using both original and augmented data. Experiments on DGT-TM for EN→NL and EN→HU show consistent, sizeable gains over NMT, Moses SMT, plain TM matching, a hybrid TM-SMT system and Google Translate. Because the architecture is unchanged, the method fits any NMT toolkit wherever a large, repetitive TM exists.

Key points

  • Data: DGT-TM (European Commission) for English→Dutch and English→Hungarian.
    • About 2.4M training pairs, two dev sets of 3000 pairs each, and a 3207-sentence test set.
    • Test sentences with exact matches were removed.
  • Augmentation formats: the source is joined with the targets of the top 1, 2 or 3 fuzzy matches via "@@@". A "format 1 n-best" variant adds a separate training pair for each of the n best matches.
  • Model: bidirectional RNN seq2seq with global attention in OpenNMT.
    • 750 LSTM units, 200K training steps, and a source vocabulary of 100K, since the input is bilingual.
  • Integration results: dedicated NFR combined with backoff NMT scored best. Unified NFR reached comparable BLEU gains over baseline NMT.
  • When NFR helps:
    • It starts to pay off at fuzzy match scores of roughly 0.5–0.6.
    • An NFR system trained on only 1/8 of the data still beat baseline NMT trained on the full set.
    • Baseline comparison differed by language: for EN-NL, SMT beat NMT by about 3 BLEU, while for EN-HU, NMT led SMT by about 7 BLEU.

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

 Institutions

🎓Ghent UniversityGhent · Belgium18 works · 2,888 citations🎓KU LeuvenLeuven · Belgium25 works · 5,716 citations

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 9,085 worksArtificial Intelligence · 8,888 worksArts and Humanities · 9,296 worksLanguage and Linguistics · 9,206 works

🧭 Topics 1Research subjects and languages the work is about.Machine translation · 958 works

Cite

Cite as Bram Bulte and Arda Tezcan. 2019. Neural Fuzzy Repair: Integrating Fuzzy Matches into Neural Machine Translation. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/p19-1175

@inproceedings{bulte2019neural,
  title = {{Neural Fuzzy Repair: Integrating Fuzzy Matches into Neural Machine Translation}},
  author = {Bram Bulte and Arda Tezcan},
  booktitle = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},
  year = {2019},
  doi = {10.18653/v1/p19-1175},
  url = {https://doi.org/10.18653/v1/p19-1175}
}

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