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<HTML>
<HEAD>
<title>Translation Task - ACL 2016 First Conference on Machine Translation</title>
<style> h3 { margin-top: 2em; } </style>
</HEAD>
<body>
<center>
<script src="title.js"></script>
<p><h2>Shared Task: Machine Translation of News</h2></p>
<script src="menu.js"></script>
</center>
<P>
The recurring translation task of the <A HREF="index.html">WMT workshops</A> focuses on
news text and European language pairs. For 2016 the language pairs are:
<ul>
<li> Czech-English
<li> German-English
<li> Romanian-English
<li> Finnish-English
<li> Russian-English
<li> Turkish-English
</ul>
The first three language pairs are sponsored by the EU Horizon2020 projects QT21 and Cracker, the Finnish-English task
is sponsored by the University of Helsinki, and funding for the last two language pairs come from Yandex.
We provide parallel corpora for all languages as
training data, and additional resources
<A HREF="#download">for download</A>.
</p>
<H3>GOALS</H3>
<p>
The goals of the shared translation task are:
<UL>
<LI>To investigate the applicability of current MT techniques when translating into languages other than English</LI>
<LI>To examine special challenges in translating between European languages, including word order differences and morphology</LI>
<LI>To investigate the translation of low-resource, morphologically rich languages</LI>
<LI>To create publicly available corpora for machine translation and machine translation evaluation</LI>
<LI>To generate up-to-date performance numbers for European languages in order to provide a basis of comparison in future research</LI>
<LI>To offer newcomers a smooth start with hands-on experience in state-of-the-art statistical machine translation methods</LI>
</UL>
We hope that both beginners and established research groups will participate in this task.
</p>
<h3>IMPORTANT DATES</h3>
<table>
<tr><td>Release of training data for shared tasks</td><td>January, 2016</td></tr>
<tr><td>Test data released</td><td>April 18, 2016</td></tr>
<tr><td>Translation submission deadline</td><td>April 24, 2016</td></tr>
<tr><td>Start of manual evaluation</td><td>May 2, 2016</td></tr>
<tr><td>End of manual evaluation (provisional)</td><td>May 22, 2016</td></tr>
<!-- <tr><td>Papers available online</td><td>TBA</td></tr> -->
</table>
<H3>TASK DESCRIPTION</H3>
<p>
We provide training data for five language pairs, and a common
framework. The task is to improve methods
current methods. This can be done in many ways. For instance participants
could try to:
<ul>
<li> improve word alignment quality, phrase extraction, phrase scoring
<li> add new components to the open source software of the baseline system
<li> augment the system otherwise (e.g. by preprocessing, reranking, etc.)
<li> build an entirely new translation systems
</ul>
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.
</P>
<p>
You may participate in any or all of the six 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.
</p>
<P>
We also strongly encourage your participation, if you use your own
training corpus, your own sentence alignment, your own language model, or
your own decoder.
</p>
<P>
If you use additional training data 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.
</p>
<p>
Your submission report should highlight in which ways your own methods
and data differ from the standard task. We may break down submitted
results in different tracks, based on what resources were used. We are
mostly interested in submission that are constrained to the provided
training data, so that the comparison is focused on the methods, not on
the data used. You may submit contrastive runs to demonstrate the benefit
of additional training data.
</p>
<H3><a name="training">TRAINING DATA</a></H3>
<p>
The provided data is mainly taken from version 7 of
the <A HREF="/europarl/">Europarl corpus</A>, which is freely available.
Please click on the links below to download the sentence-aligned data, or
go to <a href="/europarl/">the Europarl website</a> for the source
release.
Note that this the same data as last year, since Europarl is not anymore
translated across all 23 official European languages.
</p>
<P>
Additional training data is taken from the new News Commentary corpus.
There are about 50 million words of training data per language from the
Europarl corpus and 3 million words from the News Commentary corpus.
</p>
<p>
For Romanian-English and Turkish-English we have added the
<a href="http://opus.lingfil.uu.se/SETIMES2.php">SETIMES2</a> corpus to the constrained data task.
</p>
<p>
A new data resource from 2016 is the monolingual Common Crawl corpus which was collected
from web sources.
</p>
<p>
We have released development data for the Romanian-English task, and
for the Turkish-English task.
<p>
You may also use the following monolingual corpora released by the LDC:
<UL>
<LI>LDC2011T07 <a href="http://www.ldc.upenn.edu/Catalog/catalogEntry.jsp?catalogId=LDC2011T07">English Gigaword Fifth Edition</a>
<LI>LDC2009T13 <a href="http://www.ldc.upenn.edu/Catalog/catalogEntry.jsp?catalogId=LDC2009T13">English Gigaword Fourth Edition</a>
<LI>LDC2007T07 <a href="http://www.ldc.upenn.edu/Catalog/catalogEntry.jsp?catalogId=LDC2007T07">English Gigaword Third Edition</a>
</UL>
</p>
<p>
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 tools allow the processing of the training data
into tokenized format:
<UL>
<LI>Tokenizer <CODE>tokenizer.perl</CODE>
<LI>Detokenizer <CODE>detokenizer.perl</CODE>
<LI>Lowercaser <CODE>lowercase.perl</CODE>
<LI>SGML Wrapper <CODE>wrap-xml.perl</CODE>
</UL>
These tools are available in the <a href="https://github.com/moses-smt/mosesdecoder">Moses git repository</a>.
</p>
<H3><a name="dev">DEVELOPMENT DATA</a></H3>
<p>
To evaluate your system during development, we suggest using the
2015 test set. The data is provided in raw text format and in an
SGML format that suits the NIST scoring tool. We also release other
test sets from previous years.
</P>
<TABLE CELLPADDING=5 CELLSPACING=0 BORDER=1>
<tr>
<TD VALIGN=TOP width=250>
<B>news-test2008</B>
<UL>
<LI> English
<LI> French
<LI> Spanish
<LI> German
<LI> Czech
<LI> Hungarian
</UL>
Cleaned version of the 2008 test set.<br>
2051 sentences.
</TD>
<TD VALIGN=TOP width=250>
<B>news-test2009</B>
<UL>
<LI> English
<LI> French
<LI> Spanish
<LI> German
<LI> Czech
<LI> Hungarian
<LI> Italian
</UL>
2525+502 sentences.
</TD>
<TD VALIGN=TOP width=250>
<B>news-test2010</B>
<UL>
<LI> English
<LI> French
<LI> Spanish
<LI> German
<LI> Czech
</UL>
2489 sentences.
</TD>
<TD VALIGN=TOP width=250>
<B>news-test2011</B>
<UL>
<LI> English
<LI> French
<LI> Spanish
<LI> German
<LI> Czech
</UL>
3003 sentences.
</TD>
<TD VALIGN=TOP width=250>
<B>news-test2012</B>
<UL>
<LI> English
<LI> French
<LI> Spanish
<LI> German
<LI> Czech
<LI> Russian
</UL>
3003 sentences.
</TD>
<TD VALIGN=TOP width=250>
<B>news-test2013</B>
<UL>
<LI> English
<LI> French
<LI> Spanish
<LI> German
<LI> Czech
<LI> Russian
</UL>
3000 sentences.
</TD>
<TD VALIGN=TOP width=250>
<B>news-test2014</B>
<UL>
<LI> English
<LI> Czech
<LI> French
<LI> German
<LI> Hindi
<LI> Russian
</UL>
3003 sentences.
</TD>
<TD VALIGN=TOP width=250>
<B>news-test2015</B>
<UL>
<LI> English
<LI> Czech
<LI> Finnish
<LI> German
<LI> Russian
</UL>
3000 sentences.
(1500 sentences for Finnish)
</TD>
<TD VALIGN=TOP width=250>
<TABLE>
<TD>
<B>news-dev2016</B>
<UL>
<LI> Romanian
<LI> Turkish
</UL>
2000/1000 sentences.
</TD>
</TR><TR>
<TD>
<B> news-dev2015</B>
<UL>
<LI> Finnish
</UL>
1500 sentences.
</TD></TR>
</TABLE>
</TD>
</TR>
</TABLE>
<p>
The news-test2011 set has three additional Czech translations that you may want to use. You can download them
from <a href="https://ufal-point.mff.cuni.cz/xmlui/handle/11858/00-097C-0000-0008-D259-7">Charles University</a>.
</p>
<H3><A NAME="download">DOWNLOAD</a></H3>
<UL>
<LI>Parallel data:
<p></p>
<table border=1 cellpadding=2>
<tr>
<th>File</th>
<th>Size</th>
<th>CS-EN</th>
<th>DE-EN</th>
<th>FI-EN</th>
<th>RO-EN</th>
<th>RU-EN</th>
<th>TR-EN</th>
<th>Notes</th>
</tr>
<tr>
<td><A HREF="../wmt13/training-parallel-europarl-v7.tgz">Europarl v7</A></td>
<td>628MB</td>
<td align=center>✓</td> <!-- cs-en -->
<td align=center>✓</td> <!-- de-en -->
<td align=center> </td> <!-- fi-en -->
<td align=center> </td> <!-- ro-en -->
<td align=center> </td> <!-- ru-en -->
<td align=center> </td> <!-- tr-en -->
<td>same as previous year, <a href="/europarl/">corpus home page</a></td>
</tr>
<tr>
<td><A HREF="http://data.statmt.org/wmt16/translation-task/training-parallel-ep-v8.tgz">Europarl v8</A></td>
<td>215MB</td>
<td align=center> </td> <!-- cs-en -->
<td align=center> </td> <!-- de-en -->
<td align=center>✓</td> <!-- fi-en -->
<td align=center>✓</td> <!-- ro-en -->
<td align=center> </td> <!-- ru-en -->
<td align=center> </td> <!-- tr-en -->
<td>ro-en is new for this year, <a href="/europarl/">corpus home page</a></td>
</tr>
<tr>
<td><A HREF="../wmt13/training-parallel-commoncrawl.tgz"><nobr>Common Crawl corpus</nobr></A></td>
<td>876MB</td>
<td align=center>✓</td> <!-- cs-en -->
<td align=center>✓</td> <!-- de-en -->
<td align=center> </td> <!-- fi-en -->
<td align=center> </td> <!-- ro-en -->
<td align=center>✓</td> <!-- ru-en -->
<td align=center> </td> <!-- tr-en -->
<td>Same as last year</td>
</tr>
<tr>
<td><A HREF="http://data.statmt.org/wmt16/translation-task/training-parallel-nc-v11.tgz">News Commentary v11</A></td>
<td>72MB</td>
<td align=center>✓</td> <!-- cs-en -->
<td align=center>✓</td> <!-- de-en -->
<td align=center> </td> <!-- fi-en -->
<td align=center> </td> <!-- ro-en -->
<td align=center>✓</td> <!-- ru-en -->
<td align=center> </td> <!-- tr-en -->
<td>updated<!--, <a href="news-commentary-v9-by-document.tgz">data with document boundaries</a>--></td>
</tr>
<tr>
<td><a href="http://ufal.mff.cuni.cz/czeng/czeng16pre">CzEng 1.6pre</a></td>
<td>3.1GB</td>
<td align=center>✓</td> <!-- cs-en -->
<td align=center> </td> <!-- de-en -->
<td align=center> </td> <!-- fi-en -->
<td align=center> </td> <!-- ro-en -->
<td align=center> </td> <!-- ru-en -->
<td align=center> </td> <!-- tr-en -->
<td><font color="red">New for 2016.</font> <a href="http://ufal.mff.cuni.cz/czeng/czeng16pre">Register and download CzEng 1.6pre.</a></td>
</tr>
<tr>
<td><nobr>Yandex Corpus</nobr></td>
<td>121MB</td>
<td align=center> </td> <!-- cs-en -->
<td align=center> </td> <!-- de-en -->
<td align=center> </td> <!-- fi-en -->
<td align=center> </td> <!-- ro-en -->
<td align=center>✓</td> <!-- ru-en -->
<td align=center> </td> <!-- tr-en -->
<td><a href="https://translate.yandex.ru/corpus?lang=en">ru-en</a></td>
</tr>
<tr>
<td><nobr><a href="../wmt15/wiki-titles.tgz">Wiki Headlines</a></nobr></td>
<td>9.1MB</td>
<td align=center> </td> <!-- cs-en -->
<td align=center> </td> <!-- de-en -->
<td align=center>✓</td> <!-- fi-en -->
<td align=center> </td> <!-- ro-en -->
<td align=center>✓</td> <!-- ru-en -->
<td align=center> </td> <!-- tr-en -->
<td>Provided by CMU..</td>
</tr>
<tr>
<td><nobr><a href="http://opus.lingfil.uu.se/SETIMES2.php">SETIMES2</a></nobr></td>
<td>?? MB</td>
<td align=center> </td> <!-- cs-en -->
<td align=center> </td> <!-- de-en -->
<td align=center> </td> <!-- fi-en -->
<td align=center>✓</td> <!-- ro-en -->
<td align=center> </td> <!-- ru-en -->
<td align=center>✓</td> <!-- tr-en -->
<td>Distributed by <a href="http://opus.lingfil.uu.se">OPUS</a></td>
</tr>
</table>
<p></p>
<LI>Monolingual language model training data:
<p></p>
<table border=1 cellpadding=2>
<tr>
<th>Corpus</th>
<th>CS</th>
<th>DE</th>
<th>EN</th>
<th>FI</th>
<th>RO</th>
<th>RU</th>
<th>TR</th>
<th>All languages<br>combined</th>
<th>Notes</th>
</tr>
<tr>
<td><a href="/europarl/">Europarl v7/v8</a></td>
<td align=center><a href="../wmt14/training-monolingual-europarl-v7/europarl-v7.cs.gz">32MB</a></td>
<td align=center><a href="../wmt14/training-monolingual-europarl-v7/europarl-v7.de.gz">107MB</a></td>
<td align=center><a href="../wmt14/training-monolingual-europarl-v7/europarl-v7.en.gz">99MB</a></td>
<td align=center><a href="../wmt15/europarl-v8.fi.tgz">95MB</a></td> <!-- fi -->
<td align=center> </td>
<td align=center> </td> <!-- ru -->
<td align=center> </td> <!-- tr -->
<td> </td>
</tr>
<tr>
<td>News Commentary</A></td>
<td align=center><a href="http://data.statmt.org/wmt16/translation-task/news-commentary-v11.cs.gz">13MB</a></td>
<td align=center><a href="http://data.statmt.org/wmt16/translation-task/news-commentary-v11.de.gz">17MB</a></td>
<td align=center><a href="http://data.statmt.org/wmt16/translation-task/news-commentary-v11.en.gz">20MB</a></td>
<td align=center> </td> <!-- fi -->
<td align=center> </td> <!-- ro -->
<td align=center><a href="http://data.statmt.org/wmt16/translation-task/news-commentary-v11.ru.gz">17MB</a></td>
<td align=center> </td> <!-- fi -->
<td align=center><A HREF="http://data.statmt.org/wmt16/translation-task/training-monolingual-nc-v11.tgz">65MB</A></td>
<td>Updated</td>
</tr>
<tr>
<td>Common Crawl</A></td>
<td align=center><a href="http://web-language-models.s3-website-us-east-1.amazonaws.com/wmt16/deduped/cs.xz">10.5GB</a></td> <!-- cs -->
<td align=center><a href="http://web-language-models.s3-website-us-east-1.amazonaws.com/wmt16/deduped/de.xz">102GB</a></td> <!-- de -->
<td align=center><a href="http://web-language-models.s3-website-us-east-1.amazonaws.com/wmt16/deduped/en-new.xz">103 GB</a></td> <!-- en -->
<td align=center><a href="http://web-language-models.s3-website-us-east-1.amazonaws.com/wmt16/deduped/fi.xz">5.3GB</a></td> <!-- fi -->
<td align=center><a href="http://web-language-models.s3-website-us-east-1.amazonaws.com/wmt16/deduped/ro.xz">11.3GB</a></td> <!-- ro -->
<td align=center><a href="http://web-language-models.s3-website-us-east-1.amazonaws.com/wmt16/deduped/ru.xz">42GB</a></td> <!-- ru -->
<td align=center><a href="http://web-language-models.s3-website-us-east-1.amazonaws.com/wmt16/deduped/tr.xz">18GB</a></td> <!-- tr -->
<td align=center> </td>
<td><font color="red">New for 2016</font>. Deduplicated with development and evaluation sentences removed. English was updated 31 January 2016 to remove bad UTF-8. Downloads can be verified with <a href="http://data.statmt.org/ngrams/wmt16/checksums">SHA512 checksums</a>. <a href="http://data.statmt.org/ngrams/deduped_en/">More English is available for unconstrained participants.</a></td>
</tr>
<tr>
<td>News Crawl: articles from 2007</td>
<td align=center><a href="../wmt14/training-monolingual-news-crawl/news.2007.cs.shuffled.gz">3.7MB</td>
<td align=center><a href="../wmt14/training-monolingual-news-crawl/news.2007.de.shuffled.gz">92MB</td>
<td align=center><a href="../wmt14/training-monolingual-news-crawl/news.2007.en.shuffled.gz">198MB</td>
<td align=center> </td> <!-- fi -->
<td align=center> </td> <!-- ro -->
<td align=center> </td> <!-- ru -->
<td align=center> </td> <!-- tr -->
<td align=center><a href="../wmt13/training-monolingual-news-2007.tgz">302MB</a></td>
<td rowspan="10">
<p>News Crawl</p>
<p>Extracted article text from various online news publications.</p>
<p>The data sets from 2007-2014, and news-discuss, are the same
as <a href="../wmt15/translation-task.html">last year's</a>.</p>
</td>
</tr>
<tr>
<td>News Crawl: articles from 2008</td>
<td align=center><a href="../wmt14/training-monolingual-news-crawl/news.2008.cs.shuffled.gz">191MB</a></td>
<td align=center><a href="../wmt14/training-monolingual-news-crawl/news.2008.de.shuffled.gz">313MB</a></td>
<td align=center><a href="../wmt14/training-monolingual-news-crawl/news.2008.en.shuffled.gz">672MB</a></td>
<td align=center> </a></td>
<td align=center> </td> <!-- ro -->
<td align=center><a href="../wmt14/training-monolingual-news-crawl/news.2008.ru.shuffled.gz">2.3MB</a></td>
<td align=center> </td> <!-- tr -->
<td align=center><a href="../wmt13/training-monolingual-news-2008.tgz">1.5GB</a></td>
</tr>
<tr>
<td>News Crawl: articles from 2009</td>
<td align=center><a href="../wmt14/training-monolingual-news-crawl/news.2009.cs.shuffled.gz">194MB</a></td>
<td align=center><a href="../wmt14/training-monolingual-news-crawl/news.2009.de.shuffled.gz">296MB</a></td>
<td align=center><a href="../wmt14/training-monolingual-news-crawl/news.2009.en.shuffled.gz">757MB</a></td>
<td align=center> </a></td>
<td align=center> </td> <!-- ro -->
<td align=center><a href="../wmt14/training-monolingual-news-crawl/news.2009.ru.shuffled.gz">5.1MB</a></td>
<td align=center> </td> <!-- tr -->
<td align=center><a href="../wmt13/training-monolingual-news-2009.tgz">1.6GB</a></td>
</tr>
<tr>
<td>News Crawl: articles from 2010</td>
<td align=center><a href="../wmt14/training-monolingual-news-crawl/news.2010.cs.shuffled.gz">107MB</a></td>
<td align=center><a href="../wmt14/training-monolingual-news-crawl/news.2010.de.shuffled.gz">135MB</a></td>
<td align=center><a href="../wmt14/training-monolingual-news-crawl/news.2010.en.shuffled.gz">345MB</a></td>
<td align=center> </td> <!-- fi -->
<td align=center> </td> <!-- ro -->
<td align=center><a href="../wmt14/training-monolingual-news-crawl/news.2010.ru.shuffled.gz">2.5MB</a></td>
<td align=center> </td> <!-- tr -->
<td align=center><a href="../wmt13/training-monolingual-news-2010.tgz">727MB</a></td>
</tr>
<tr>
<td>News Crawl: articles from 2011</td>
<td align=center><a href="../wmt14/training-monolingual-news-crawl/news.2011.cs.shuffled.gz">389MB</a></td>
<td align=center><a href="../wmt14/training-monolingual-news-crawl/news.2011.de.shuffled.gz">746MB</a></td>
<td align=center><a href="../wmt14/training-monolingual-news-crawl/news.2011.en.shuffled.gz">784MB</a></td>
<td align=center> </td> <!-- fi -->
<td align=center> </td> <!-- ro -->
<td align=center><a href="../wmt14/training-monolingual-news-crawl/news.2011.ru.shuffled.gz">564MB</a></td>
<td align=center> </td> <!-- tr -->
<td align=center><a href="../wmt13/training-monolingual-news-2011.tgz">3.1GB</a></td>
</tr>
<tr>
<td>News Crawl: articles from 2012</td>
<td align=center><a href="../wmt14/training-monolingual-news-crawl/news.2012.cs.shuffled.gz">337MB</a></td>
<td align=center><a href="../wmt14/training-monolingual-news-crawl/news.2012.de.shuffled.gz">946MB</a></td>
<td align=center><a href="../wmt14/training-monolingual-news-crawl/news.2012.en.shuffled.gz">751MB</a></td>
<td align=center> </td> <!-- fi -->
<td align=center> </td> <!-- ro -->
<td align=center><a href="../wmt14/training-monolingual-news-crawl/news.2012.ru.shuffled.gz">568MB</a></td>
<td align=center> </td> <!-- tr -->
<td align=center><a href="../wmt13/training-monolingual-news-2012.tgz">3.1GB</a></td>
</tr>
<tr>
<td>News Crawl: articles from 2013</td>
<td align=center><a href="../wmt14/training-monolingual-news-crawl/news.2013.cs.shuffled.gz">395MB</a></td>
<td align=center><a href="../wmt14/training-monolingual-news-crawl/news.2013.de.shuffled.gz">1.6GB</a></td>
<td align=center><a href="../wmt14/training-monolingual-news-crawl/news.2013.en.shuffled.gz">1.1GB</a></td>
<td align=center> </td> <!-- fi -->
<td align=center> </td> <!-- ro -->
<td align=center><a href="../wmt14/training-monolingual-news-crawl/news.2013.ru.shuffled.gz">730MB</a></td>
<td align=center> </td> <!-- tr -->
<td align=center><a href="../wmt14/training-monolingual-news-2013.tgz">4.3GB</a></td>
</tr>
<tr>
<td>News Crawl: articles from 2014</td>
<td align=center><a href="../wmt15/training-monolingual-news-crawl-v2/news.2014.cs.shuffled.v2.gz">380MB</a></td>
<td align=center><a href="../wmt15/training-monolingual-news-crawl-v2/news.2014.de.shuffled.v2.gz">2.1GB</a></td>
<td align=center><a href="../wmt15/training-monolingual-news-crawl-v2/news.2014.en.shuffled.v2.gz">1.4GB</a></td>
<td align=center><a href="../wmt15/training-monolingual-news-crawl-v2/news.2014.fi.shuffled.v2.gz">52MB</a></td>
<td align=center> </td> <!-- ro -->
<td align=center><a href="../wmt15/training-monolingual-news-crawl-v2/news.2014.ru.shuffled.v2.gz">801MB</a></td>
<td align=center> </td> <!-- tr -->
<td align=center><a href="../wmt15/training-monolingual-news-2014.v2.tgz">5.3GB</a></td>
</tr>
<tr>
<td>News Discussions. Version 1 from 2014/15
<td align=center></td>
<td align=center></td>
<td align=center><a href="../wmt15/news-discuss-v1.en.txt.gz">1.7GB</a></td>
<td align=center></td>
<td align=center></td>
<td align=center></td>
<td align=center></td>
<td align=center></td>
</tr>
<tr>
<td>News Crawl: articles from 2015</td>
<td align=center><a href="http://data.statmt.org/wmt16/translation-task/news.2015.cs.shuffled.gz">360MB</a></td>
<td align=center><a href="http://data.statmt.org/wmt16/translation-task/news.2015.de.shuffled.gz">2.2GB</a></td>
<td align=center><a href="http://data.statmt.org/wmt16/translation-task/news.2015.en.shuffled.gz">1.3GB</a></td>
<td align=center><a href="http://data.statmt.org/wmt16/translation-task/news.2015.fi.shuffled.gz">203MB</a></td>
<td align=center><a href="http://data.statmt.org/wmt16/translation-task/news.2015.ro.shuffled.gz">125MB</a></td>
<td align=center><a href="http://data.statmt.org/wmt16/translation-task/news.2015.ru.shuffled.gz">608MB</a></td>
<td align=center> </td>
<td align=center><a href="http://data.statmt.org/wmt16/translation-task/training-monolingual-news-crawl.tgz">4.8G</a></td>
</tr>
</table>
<p>The Common Crawl monolingual data is hosted by Amazon Web Services as a <a href="https://aws.amazon.com/public-data-sets/">public data set</a>. The underlying S3 URL is <code>s3://web-language-models/wmt16/deduped</code>.</p>
<LI><A HREF="http://data.statmt.org/wmt16/translation-task/dev.tgz">Development sets</A> (22 MB)
<LI><A HREF="http://data.statmt.org/wmt16/translation-task/dev-romanian-updated.tgz">Updated Romanian dev, with fixed diacritics</A> (0.5 MB)
<!-- <LI><A HREF="test.tgz">Test sets</A> (5.2 MB) These are the source sgm files with extra "filler" sentences. They were the actual files released for the campaign.
<LI><font color=red><b>NEW:</b></font> <A HREF="test.tgz">Test sets (source and reference)</a> (2MB) </li> -->
<H3><a name="submission">TEST SET SUBMISSION</a></H3>
Punctuation in the official test sets will be encoded with ASCII characters (not complex Unicode characters) as much as possible. You may want to <A HREF="../wmt11/normalize-punctuation.perl">normalize</A> your system's output before submission.
<p>
To submit your results, please first convert into into SGML format as
required by the NIST BLEU scorer, and then upload it to the
website <A HREF="http://matrix.statmt.org/">matrix.statmt.org</A>.
</p>
<H4 id="sgml">SGML Format</H4>
<p>
Each submitted file has to be in a format that is used by standard
scoring scripts such as NIST BLEU or TER.
</p>
<P>
This format is similar to the one used in the source test set files that
were released, except for:
<UL>
<LI>
First line is <CODE><tstset trglang="en" setid="newstest2015"
srclang="any"></CODE>, with trglang set to
either <CODE>en</CODE>, <CODE>de</CODE>, <CODE>fr</CODE>, <CODE>es</CODE>,
<CODE>cs</CODE> or <CODE>ru</CODE>. Important: srclang is
always <CODE>any</CODE>.
<LI>
Each document tag also has to include the system name,
e.g. <CODE>sysid="uedin"</CODE>.
<LI>
CLosing tag (last line) is <CODE></tstset></CODE>
</UL>
</p>
<p>
The script <A HREF="../wmt11/wrap-xml.perl">wrap-xml.perl</A> makes the conversion
of a output file in one-segment-per-line format into the required SGML
file very easy:
</p>
<P>
Format: <CODE>wrap-xml.perl LANGUAGE SRC_SGML_FILE SYSTEM_NAME < IN > OUT</CODE><BR>
Example: <CODE>wrap-xml.perl en newstest2016-src.de.sgm Google < decoder-output > decoder-output.sgm</CODE>
</p>
<H4>Upload to Website</H4>
<p>Upload happens in three easy steps:</p>
<OL>
<LI>Go to the website <A HREF="http://matrix.statmt.org/">matrix.statmt.org</A>.
<LI>Create an account under the menu item Account -> Create Account.
<LI>Go to Account -> upload/edit content, and follow the link "Submit a system run"
<UL><LI>select as test set "newstest2015" and the language pair you are submitting
<LI>select "create new system"
<LI>click "continue"
<LI>on the next page, upload your file and add some description
</UL>
</OL>
<p>
If you are submitting contrastive runs, please submit your primary system
first and mark it clearly as the primary submission.
</p>
<H3>EVALUATION</H3>
<p>Evaluation will be done both automatically as well as by human judgement.</p>
<UL>
<LI>
Manual Scoring: We will collect subjective judgments about translation
quality from human annotators. If you participate in the shared task,
we ask you to perform a defined amount of evaluation per language pair
submitted. The amount of manual evaluation is TBD for 2016.
<!-- The judgements will be collected in "HITs", where each HIT
consists of ranking the outputs of 5 different systems for 3 sentences. Each
team will be expected to complete 100 HITs per language pair.
The evaluation will be done with an online tool. -->
<LI>
As in previous years, we expect the translated submissions to be in
recased, detokenized, XML format, just as in most other translation
campaigns (NIST, TC-Star).
</UL>
<!-- <p align=right>
<a href="http://www.euromatrixplus.net/"><img align=right src="EMplus_100px.png" border=0 width=100 height=100></a>
supported by the <a href="http://www.euromatrixplus.net/">EuroMatrixPlus</a> project<BR>P7-IST-231720-STP<BR> funded by the European Commission<BR> under Framework Programme 7
</p> -->
<h3>ACKNOWLEDGEMENTS</h3>
This conference has received funding
from the European Union's Horizon 2020 research
and innovation programme under grant agreements
645452 (<a href="http://www.qt21.eu/">QT21</a>) and 645357 (<a href="http://www.meta-net.eu/projects/cracker/">Cracker</a>).
<br>
We thank <a href="http://www.yandex.com">Yandex</a> for their donation of data for the Russian-English and Turkish-English news tasks, and the University of Helsinki for their donation for the Finnish-English news tasks.
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