Can Synthetic Translations Improve Bitext Quality?

Eleftheria Briakou; Marine Carpuat

ACL 2022long6 citations

Can Synthetic Translations Improve Bitext Quality?

Abstract

Synthetic translations have been used for a wide range of NLP tasks primarily as a means of data augmentation. This work explores, instead, how synthetic translations can be used to revise potentially imperfect reference translations in mined bitext. We find that synthetic samples can improve bitext quality without any additional bilingual supervision when they replace the originals based on a semantic equivalence classifier that helps mitigate NMT noise. The improved quality of the revised bitext is confirmed intrinsically via human evaluation and extrinsically through bilingual induction and MT tasks.

BibTeX

@inproceedings{briakou-carpuat-2022-synthetic,
    title = "Can Synthetic Translations Improve Bitext Quality?",
    author = "Briakou, Eleftheria  and
      Carpuat, Marine",
    editor = "Muresan, Smaranda  and
      Nakov, Preslav  and
      Villavicencio, Aline",
    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.acl-long.326/",
    doi = "10.18653/v1/2022.acl-long.326",
    pages = "4753--4766"
}