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Findings of the 2021 Conference on Machine Translation (WMT21)

Written for the Research library (claude-opus-5-5) from the paper text. Original — aclanthology.org/2021.wmt-1.1.pdf

Summary

WMT21 is the yearly shared-evaluation campaign for machine translation. This report brings together the results of several of its tasks. The largest is the news task, which had 20 translation directions. Six of these did not involve English. New pairs included Hausa and Icelandic paired with English, plus Bengali↔Hindi and Xhosa↔Zulu.

Organizers built fresh source-original test sets and collected human direct assessment (DA) scores on a 100-point scale. They compared several annotation setups that differed in how much document context annotators could see. When annotators saw full documents, the human reference held up well. When sentences were judged in isolation, top systems often outscored it.

The report also covers four other tasks:

  • similar-language translation (Dravidian, Romance, French→Bambara/Maninka)
  • Russian→Chinese triangular MT
  • multilingual low-resource translation in the cultural heritage domain
  • automatic post-editing (APE)

All test data, system outputs and judgments are publicly released. This makes WMT21 a key benchmark for MT and evaluation research.

Key points

  • News task setup: 20 directions, 14 with English and 6 without. Test sets targeted about 1000 sentences, with 1948 for Chinese→English. Bengali↔Hindi and Xhosa↔Zulu used FLORES-101 data of roughly 500 sentences.
  • Evaluation variants: Human evaluation used several DA modes (SR+DC, SR+FD, SR−DC, contrastive pairs). German→English was additionally scored with the GENIE leaderboard. MQM scores for en→de, en→ru and zh→en appear in the metrics task paper.
  • MLLR task: It covered North-Germanic languages (Icelandic, Norwegian Bokmål, Swedish) and Romance languages (Catalan, Italian, Occitan, Romanian), using Europeana and Wikipedia texts. The best results came from multilingual models with backtranslation and same-family high-resource data. Pivoting and in-domain data selection gave no benefit.
  • APE task: It used English-German and English-Chinese Wikipedia data. Only two teams took part, both in English-German only. A single run significantly beat the do-nothing baseline on TER, yet human judges preferred every submission.
  • Test suites: Five were collected and two were analysed in time for the proceedings. The German↔English linguistic test suite found big gains on idioms. False friends, ambiguity and multi-word expressions remained hard.

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

189 papers of our library cite it (of the 140673 whose bibliographies we read); 8 of them have a page here, 181 not yet.

Cite

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

@inproceedings{akhbardeh2021findings,
  title = {{Findings of the 2021 Conference on Machine Translation (WMT21)}},
  author = {Akhbardeh, Farhad and Arkhangorodsky, Arkady and Biesialska, Magdalena and Bojar, Ondřej and Chatterjee, Rajen and Chaudhary, Vishrav and Costa-jussa, Marta R. and España-Bonet, Cristina and Fan, Angela and Federmann, Christian and Freitag, Markus and Graham, Yvette and Grundkiewicz, Roman and Haddow, Barry and Harter, Leonie and Heafield, Kenneth and Homan, Christopher M. and Huck, Matthias and Amponsah-Kaakyire, Kwabena and Kasai, Jungo and Khashabi, Daniel and Knight, Kevin and Kocmi, Tom and Koehn, Philipp and Lourie, Nicholas and Monz, Christof and Morishita, Makoto and Nagata, Masaaki and Nagesh, Ajay and Nakazawa, Toshiaki and Negri, Matteo and Pal, Santanu and Tapo, Allahsera Auguste and Turchi, Marco and Vydrin, Valentin and Zampieri, Marcos},
  booktitle = {Proceedings of the Sixth Conference on Machine Translation},
  year = {2021},
  url = {https://aclanthology.org/2021.wmt-1.1/}
}

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