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Findings of the 2018 Conference on Machine Translation (WMT18)

Written for the Research library (claude-opus-5-5) from the paper text. Original — aclanthology.org/W18-6401.pdf

Summary

WMT18 was the third Conference on Machine Translation. This paper reports on its main shared task: translating news text between English and seven languages (Chinese, Czech, Estonian, German, Finnish, Russian, Turkish) in both directions, which makes 14 tasks. Estonian was new this year and served as a lower-resource pair. The organizers commissioned professional translations of fresh news articles to build the test sets. Training resources included parallel corpora such as Europarl, ParaCrawl, CzEng v1.7, the new EU Press Release corpus and several new Chinese collections, plus large monolingual corpora. They added two sub-tracks, multilingual and unsupervised MT, and invited "test suites" that probe specific linguistic phenomena. The official ranking came from human Direct Assessment (DA) of adequacy on a 100-point scale, collected from participating researchers and Mechanical Turk workers. Automatic metrics were reported alongside. In a source-based DA for English→Czech, top neural systems scored higher than the human reference. The authors warn that this does not establish human parity. All data, system outputs and judgments were released publicly, giving the community a benchmark for MT, evaluation and quality estimation research.

Key points

  • 103 submissions came from 32 institutions (35 teams). They were compared with 39 anonymized online systems drawn from 5 commercial services.
  • Each test set has about 3,000 sentences: half written originally in English, half in the other language. For Estonian, 2,000 test and 2,000 dev sentences were translated from scratch.
  • New or updated data included the ParaCrawl corpus (e.g., 36M German–English sentence pairs), the EU Press Release corpus, the BigEst Estonian corpus, and Chinese corpora such as casia2015, casict2015, datum2017 and neu2017.
  • In source-based DA for English→Czech, CUNI-Transformer averaged 84.4 and UEDIN 79.8, both above NEWSTEST2018-REF at 78.6.
  • The caveats on that comparison are:
    • evaluation was sentence-level only, without document context;
    • judgment samples were smaller than recommended;
    • test data mixed both original translation directions;
    • some references were of uneven quality;
    • the assessment setup (HIT design) was new and not yet fully validated.

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 · Machine translation

Topics: Machine translation

Cited by

227 papers of our library cite it (of the 140673 whose bibliographies we read); 20 of them have a page here, 207 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/w18-6401

@inproceedings{bojar2018findings,
  title = {{Findings of the 2018 Conference on Machine Translation (WMT18)}},
  author = {Bojar, Ondřej and Federmann, Christian and Fishel, Mark and Graham, Yvette and Haddow, Barry and Huck, Matthias and Koehn, Philipp and Monz, Christof},
  booktitle = {Proceedings of the Third Conference on Machine Translation: Shared Task Papers},
  year = {2018},
  doi = {10.18653/v1/w18-6401},
  url = {https://doi.org/10.18653/v1/w18-6401}
}

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