Shared Task: Machine Translation of News

NB: All systems, test sets and evaluations are available from this repository

The recurring translation task of the WMT workshops focuses on news text. For this year the language pairs are:

We provide parallel corpora for all languages as training data, and additional resources for download.

The following is a quick guide to the language pairs (in terms of resource-level and language similarity)

High resourceMedium resourceLow resource
Closely-related bn-hixh-zu
Same familyen-de, en-cs, en-ruen-is, fr-de


The goals of the shared translation task are:

We hope that both beginners and established research groups will participate in this task.


Release of training data for shared tasks (by)March, 2021
Test suite source texts must reach us 27 May 2021
Test data released 10 June 2021
Translation submission deadline17 June 2021
Translated test suites shipped back to test suites authors 30 June 2021
Start of manual evaluationTBC
End of manual evaluation TBC


We provide training data for all language pairs, and a common framework. The task is to improve current methods. We encourage a broad participation -- if you feel that your method is interesting but not state-of-the-art, then please participate in order to disseminate it and measure progress. Participants will use their systems to translate a test set of unseen sentences in the source language. The translation quality is measured by a manual evaluation and various automatic evaluation metrics. Participants agree to contribute to the manual evaluation about eight hours of work, per system submission.

You may participate in any or all of the language pairs. For all language pairs we will test translation in both directions. To have a common framework that allows for comparable results, and also to lower the barrier to entry, we provide a common training set. You are not limited to this training set, and you are not limited to the training set provided for your target language pair. This means that multilingual systems are allowed, and classed as constrained as long as they use only data released for WMT21.

If you use additional training data (not provided by the WMT21 organisers) or existing translation systems, you must flag that your system uses additional data. We will distinguish system submissions that used the provided training data (constrained) from submissions that used significant additional data resources. Note that basic linguistic tools such as taggers, parsers, or morphological analyzers are allowed in the constrained condition.

Your submission report should highlight in which ways your own methods and data differ from the standard task. You should make it clear which tools you used, and which training sets you used.

Document-level MT

We are interested in the question of whether MT can be improved by using context beyond the sentence, and to what extent state-of-the-art MT systems can produce translations that are correct "in-context" All of our development and test data contains full documents, and all our human evaluation will be in-context, in other words the evaluators will view the sentence as well as its surrounding context when evaluating.

Our training data retains context and document boundaries wherever possible, in particular the following corpora retain the context intact:

Additional Test Suites Linked to News Translation Task

Test Suites follow the established format from previous years.

Please see the details here:



The data released for the WMT21 news translation task can be freely used for research purposes, we just ask that you cite the WMT21 shared task overview paper, and respect any additional citation requirements on the individual data sets. For other uses of the data, you should consult with original owners of the data sets.


We aim to use publicly available sources of data wherever possible. Our main sources of training data are the Europarl corpus, the UN corpus, the news-commentary corpus and the ParaCrawl corpus. We also release a monolingual News Crawl corpus. Other language-specific corpora will be made available.

We have added suitable additional training data to some of the language pairs.

You may also use the following monolingual corpora released by the LDC:

Note that the released data is not tokenized and includes sentences of any length (including empty sentences). All data is in Unicode (UTF-8) format. The following Moses tools allow the processing of the training data into tokenized format:

These tools are available in the Moses git repository.


Download here (Updated: 2021-04-07 with extra ha-en data, 2021-04-14 with Hindi, Bengali, Zulu, Xhosa dev sets)

To evaluate your system during development, we suggest using previous test sets. Evaluation can be performed with the NIST tool or (recommended) with sacreBLEU, which will automatically download previous WMT test sets for you. We also release other dev and test sets from previous years. For the new language pairs, we release dev sets in February, prepared in the same way as the test sets.

2021 (dev)            

The 2021 test sets will be created from a sample of online newspapers from July-October 2020. The sources of the test sets will be original text from the online news, whereas the targets will be human-produced translations. This is in contrast to the test sets up to and including 2018, which were a 50-50 mixture of test sets produced in this way, and test sets produced in the reverse direction (i.e. with the original text on the target side).

We have released development data for the tasks that are new this year. It is created in the same way as the test set and included in the development tarball. Note that the dev data contains both forward and reverse translations (clearly marked).

We are switching to an xml format (instead of the previous sgm format) for all dev, test and submission files. It is important to use an xml parser to wrap/unwrap text in order to ensure correct escaping/de-escaping. We will provide tools.

The news-test2011 set has three additional Czech translations that you may want to use. You can download them from Charles University.

Extra references (both translated and paraphrased) for the English to German WMT19 test set have been contributed by Google Research.

NBThe cs-en version of newstest2020 should not be used during system development. It has not been included in the development tarball




All primary systems will be included in the human evaluation. We will collect subjective judgments about the translation qaility from annotators, taking the document context into account.

If you participate in the shared task, then you must provide a defined amount of evaluation per language pair submitted. The amount of manual evaluation will be 10 hours per language pair, per team. You can provide evaluation of any of the task's languages, regardless of which language pair you submitted systems to.


For queries, please use the mailing list or contact


This task would not have been possible without the sponsorship of test sets and evaluation from Microsoft, Facebook, Yandex, NTT, the University of Tokyo, LinguaCustodia as well as funding from the European Union's Horizon 2020 research and innovation programme under grant agreement 825299 (GoURMET) and 825460 (Elitr).

The Icelandic-English task is sponsored by the Language Technology Programme for Icelandic 2019–2023. The programme, which is managed and coordinated by Almannarómur, is funded by the Icelandic Ministry of Education, Science and Culture.

WMT21 Collaboration with Toloka

For WMT21 we have partnered with Toloka to collect more annotations for the human evaluation of the news translation shared task. We are grateful for their support and look forward to our continued collaboration in the future!

In Toloka's own words:

The international data labeling platform Toloka collaborated with the WMT team to improve existing machine translation methods. Toloka's crowdsourcing service was integrated with Appraise, an open-source framework for human-based annotation tasks.

To increase the accuracy of machine translation, we need to systematically compare different MT methods to reference data. However, obtaining sufficient reference data can pose a challenge, especially for rare languages. Toloka solved this problem by providing a global crowdsourcing platform with enough annotators to cover all relevant language pairs. At the same time, the integration preserved the labeling processes that were already set up in Appraise without breaking any tasks.

Collaboration between Toloka and Appraise made it possible to get a relevant pool of annotators, provide them with an interface for labeling and getting rewards, and then combine quality control rules from both systems into a mutually reinforcing set for reliable results.

You can learn more about Toloka on their website: