Welcome to the WMT 2022 Chat Shared Task!


October 16th, 2022

The test sets with the gold references are now available here.

September 6th, 2022

The results of the automatic evaluations are published. You can check them in the Automatic evaluation (results) section.

July 28th, 2022

The test sets are now available here. Please check the updated details of the submission information in the Submission format section.

July 22nd, 2022

The evaluation week was postponed by 1 week: it will now happen between 28th July - 6th August. Test sets will be available for download on the 28th of July.

More updated information can be found in the Submission format section.

July 7th, 2022

Baselines scores on devsets are now available in the section Baseline Scores on DevSets below and scripts to reproduce them can be found here.

June 30th, 2022

Please also register to the chat-shared-task google-group in order to be able to receive immediate updates announcements and ask us questions!

Baselines’ scores on devsets will be available soon!

List of additional permited training data are updated in the Datasets section.


Translating conversational text, in particular customer support chats, is an important and challenging application for machine translation technology. This type of content has so far not been extensively explored in prior MT research, largely due to the lack of publicly available data sets. Prior related work has mostly focused on movie subtitles and European Parliament speeches.
In contrast to the translation of the news stories, software manuals, biomedical text, etc. in which the text is carefully authored and well formated, chat conversations are less planned, more informal, and often ungrammatical. Further, such conversations are usually characterized by shorter and simpler sentences and contain more pronouns.
In effect, the task of translating chat conversations can be regarded as a two-in-one task, modelling both dialogue and translation at the same time.

Machine translation systems trained for chat conversations are expected to deal with the task’s inherent challenges and characteristics, such as (among others):

The primary goal of this Chat shared task is to encourage participants to train and test models specific for bilingual chat conversations in a corpus composed of original bilingual costumer support conversations.

This year we expanded the language pairs to en⇔de, en⇔fr, and en⇔pt_br.

We encourage participants to use the bilingual context in the translation models submissions.

Have questions or suggestions? Feel free to Contact Us!

Chat Task Important Dates

Validation set ready to download 15th 22nd June
Test set ready to download 23rd 28th July, 2022
Submission deadline for Chat task 23-28th 28th July - 6th August, 2022
Paper submission deadline to WMT 7th September, 2022
WMT Notification of acceptance 9th October, 2022
WMT Camera-ready deadline 16th October, 2022
Conference 7th - 8th December, 2022


The goals of chat translation shared task are to provide the common ground for:

Task Description

A critical challenge faced by international companies today is delivering customer support in several different languages. One solution to this challenge is centralizing support with English speaking agents and having a translation layer in the middle to translate from the customer’s language into the agent’s (English) and vice versa.


The data used in this shared task is part of a unique corpus called MAIA corpus that is composed with genuin bilingual costumer support conversations. One of the main challenges of this domain is the lack of real bilingual training data available. In order to make this shared task as close as possible to the real-world settings, this year, we release only the dev and test sets. So, please note that there will be no training data for this edition and the participants are expected to use only the dev sets (bilingual and monolingual) to train and fine-tune their MT systems.

Please note, that all the data released for the WMT22 Chat Translation task is under the license of CC-BY-NC-4.0 and can be freely used for research purposes only. Please note that, as the license states, no commercial uses are permitted for this corpus. We just ask that you cite the WMT22 Chat Translation Task overview paper. Any other use is not permitted unless previous written authorization is given by Unbabel.


  1. Zero-shot: In this task participants have to develop machine translation systems without access to any in-domain training data.
  2. Low-resource: In this task participants have to develop machine translation systems with access to bilingual conversations without reference translations.


The Dev sets (both bilingual and monolingual) of all the language pairs are available in the github repository. The files contain the bilingual conversations by a customer and an agent in their original language. Here is an example of such a conversation:

source_language mt_language source_segment pe_segment translation_direction
fr en Bonjour je ne peut pas utiliser mes jetons accumuler pour acheter un livre audio Hello, I can’t use the tokens I accumulated to buy an audio book. customer
en fr Thank you for contacting #PRS_ORG#, it was my pleasure to assist you today. Merci d’avoir contacté #PRS_ORG#, ce fut un plaisir de vous aider aujourd’hui. agent
en fr I hope you have an excellent day. J’espère que vous passez une excellente journée. agent
en fr Please allow me a moment to verify the account. Veuillez m’accorder un moment pour vérifier le compte. agent
en fr I am sorry that you are experiencing this issue, I will do my best to assist you. Je suis désolé que vous rencontriez ce problème, je ferai de mon mieux pour vous aider. agent
en fr What is the error message or problem you are getting when trying to use your audio book credits? Quel est le message d’erreur ou le problème que vous rencontrez lorsque vous essayez d’utiliser vos crédits de livres audio ? agent
fr en Je ne peu pas effectuer l achat je peu juste le faire avec #PRS_ORG# ou par carte I am unable to make the purchase, I can only do it with #PRS_ORG# or by card. customer
en fr Are your credit s from an #PRS_ORG# subscription? Vos crédits proviennent-ils d’un abonnement #PRS_ORG# ? agent
fr en Oui Yes. customer
en fr I understand, at the moment we no longer work with #PRS_ORG#, they are not partnered with us anymore, probably that is the error, please let me confirm this information Je comprends, pour le moment nous ne travaillons plus avec #PRS_ORG#, ils ne sont plus en partenariat avec nous, c’est probablement la raison de l’erreur, laissez-moi confirmer ces informations agent
fr en Mais ça me met que j ai 13 jetons à utiliser But it shows that I have 13 tokens. customer
en fr I am confirming the information with my team, thanks for your patience Je confirme les informations avec mon équipe, merci pour votre patience agent

As you can see, the source sentences (i.e. source_segment) are in the original language of the speaker, i.e. French in the case of customer and English in the case of agent. And the translations (i.e. pe_segment). Moreover, since the data is anonymised, we have the entities replaced by the following special tokens:

Token Description
#NAME# Person’s names
#PRS_ORG# Products and Services, and Organizations
#ADDRESS# Address
#EMAIL# E-mail address
#IP# IP Address
#PASSWORD# Password
#PHONENUMBER# Phone number
#CREDITCARD# Credit card number
#URL# URL Address
#IBAN# IBAN number
#NUMBER# Any number (all digits)
#ALPHANUMERIC_ID# Any alphanumeric ID

Note that for training and validation purposes you can use the training data of the general task (including the data of the previous editions), the data of the other tracks (eg. biomedical) if you find them useful for this task, and the other corpora (either parallel or monolingual) that are publicly available for the research purposes, like most of the corpora available on OPUS, as well as the data of the previous edition of the Chat Translation Task.

Baseline Scores on DevSets

We provide two different baselines: one without any context and another with context (using the two previous iterations for the corresponding direction).

Baseline without context:

Direction Lang BLEU chrF COMET TER
agent de 35.24 57.17 0.4168 55.70
agent fr 54.14 69.47 0.7984 37.95
agent pt_br 50.38 68.84 0.8645 40.99
customer de 45.98 60.81 0.5426 51.21
customer fr 46.51 62.29 0.6382 50.12
customer pt_br 44.71 59.95 0.5851 53.78

Baseline with context:

Direction Lang BLEU chrF COMET TER
agent de 33.89 55.96 0.3811 57.30
agent fr 53.58 68.81 0.7978 41.50
agent pt_br 49.94 67.95 0.9029 40.89
customer de 47.11 62.06 0.6163 46.94
customer fr 48.05 63.61 0.6834 47.78
customer pt_br 47.24 62.31 0.6437 47.91

Scripts to reproduce scores are available here.

Test Sets (Evaluation Data)

Test Sets will be available on the 23rd 28th July of July.

Submission Format

Submissions will be done by sending us an email to our official email.

Each team can submit at most 3 MT outputs per language pair direction, one primary and up to two contrastive. The submitting team is required to explicitly indicate which of these submissions represents their primary submission. In the case that the participating team sends several submissions (in different emails) only the latest submission received will be used as their official submission.

The submission file names should be set according to the follwong template: <Team-Name>_<SourceLanguage>-<TargetLanguage>_<SubmissionType>.csv

The <SubmissionType> can be one of the followings: Primary, Contrastive1, and Contrastive2.

We released a converter along with test sets that converts the outputs of the MT systems (in the text format) into the CSV format. Participants are encouraged to translate both directions (i.e. agent and customer) but can choose to only submit translations to one direction. For more details on the converter script (the file formats, directions, etc) check the README.md file provided in our github repository.

Note 1: Translations should be “human-ready”, i.e. in the form that text is normally published, so latin-script languages should be recased and detokenised.

Note 2: Each participating team is asked to send a short paragraph briefly describing their approach, along with their submissions.


Similarly to the previous edition, the Systems’ performance will be evaluated both automatically and manualy. But, this year we will use COMET as the automatic metric and MQM for the human evaluation.
The manual document-level evaluation will be performed only on the primary submission and will be used for the official rankings of the participating teams accounting for both directions. The COMET scores will be used as the secondary metric.

Automatic evaluation (results)

We used COMET and chrF for the automatica evaluation. Below are presented the results:


  en-de   de-en  
Baseline without context 0.403 0.550 0.588 0.621
Baseline with context 0.376 0.537 0.680 0.642
BJTU-WeChat 0.810 0.735 0.946 0.775
Unbabel 0.774 0.733 0.915 0.737
IITP-Flipkart 0.768 0.730 0.907 0.729
Huawei 0.704 0.725 0.918 0.766


  en-fr   fr-en  
Baseline without context 0.644 0.640 0.574 0.587
Baseline with context 0.664 0.631 0.601 0.602
Unbabel 1.086 0.838 0.838 0.677


  en-pt_br   pt_br-en  
Baseline without context 0.824 0.681 0.61 0.631
Baseline with context 0.863 0.675 0.675 0.653
Unbabel 1.078 0.771 0.849 0.689

Paper Describing Your MT Systems

Participants are invited to submit a short paper (4 to 6 pages) to WMT describing their MT system. Information on how to submit is available here.
Please note that the shared task submission description papers are non-archival, and it is not mandatory to submit a paper if you do not want to.