This workshop builds on six previous workshops on statistical machine translation, which is one of the most prestigious venues for research in computational linguistics:
|Release of training data||Early February 2013|
|Test set distributed for translation task||April 29, 2013|
|Submission deadline for translation task||May 3 2013|
|System outputs distributed for metrics task||May 10, 2013|
|Submission deadline for metrics task||May 31, 2013|
|Paper submission deadline||June 7, 2013|
|Start of manual evaluation period||May 17, 2013|
|Notification of acceptance||June 24, 2013|
|End of manual evaluation||June 7, 2013|
|Camera-ready deadline||July 5, 2013|
This year's workshop will feature three shared tasks:
In addition to the shared tasks, the workshop will also feature scientific papers on topics related to MT. Topics of interest include, but are not limited to:
The first shared task which will examine translation between the following language pairs:
All participants who submit entries will have their translations evaluated. We will evaluate translation performance by human judgment. To facilitate the human evaluation we will require participants in the shared tasks to manually judge some of the submitted translations.
We also provide baseline machine translation systems, with performance comparable to the best systems from last year's shared task.
A topic of increasing interest in MT is that of estimating the quality of translated texts. Different from MT evaluation, quality estimation (QE) systems do not rely on reference translations, but rather predict the quality of an unseen translated text (document, sentence, phrase) at system run-time. This topic is particularly relevant from a user perspective: among other applications, it can (i) help decide whether a given translation is good enough for publishing as is (Soricut and Echihabi, 2010); (ii) filter out sentences that are not good enough for post-editing (Specia, 2011); (iii) select the best translation among options from multiple MT and/or translation memory systems (He et al., 2010); and (iv) inform readers of the target language of whether or not they can rely on a translation (Specia et al., 2011).
Although still very recent, research in this topic has been showing promising results in the last couple of years. However, efforts are scattered around several groups and, as a consequence, comparing different systems is difficult as there are neither well established baselines nor standard evaluation metrics. In the Quality-Estimation track of the WMT workshop and shared-task, we will provide training and test sets, along with evaluation metrics and a baseline system. By providing a common ground for development and comparison, we expect to foster research in the topic, as well as to attract new people interested in the subject, who can build and evaluate new solutions using the provided resources.
Participants in the shared evaluation task will use their automatic evaluation metrics to score the output from the translation task and the system combination task. They will be provided with the output from the other two shared tasks along with reference translations. We will measure the correlation of automatic evaluation metrics with the human judgments.
Submissions will consist of regular full papers of 6-10 pages, plus additional pages for references, formatted following the ACL 2013 guidelines. In addition, shared task participants will be invited to submit short papers (4-6 pages) describing their systems or their evaluation metrics. Both submission and review processes will be handled electronically.
We encourage individuals who are submitting research papers to evaluate their approaches using the training resources provided by this workshop and past workshops, so that their experiments can be repeated by others using these publicly available corpora.
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|You can read past announcements on the Google Groups page for WMT13. These also include an archive of annoucements from earlier workshops.|
For questions, comments, etc. please send email to firstname.lastname@example.org.
Supported by the European Commision
project (grant number 288487)