ACL 2008

June 19, 2008


Participate in the manual evaluation.

This workshop on statistical and hybrid methods for machine translation, and builds on the 2005 ACL Workshop on Parallel Text, the 2006 NAACL Workshop on Statistical Machine Translation, and the 2007 ACL Second Workshop on Statistical Machine Translation. The workshop will feature papers on topics related to MT, and will feature two shared tasks: a shared translation task for 12 pairs of European languages, and a shared evaluation task to test automatic evaluation metrics.

Topics of interest include, but are not limited to:

We encourage authors to evaluate their approaches to the above topics using the common data sets created for the shared translation task. In addition to scientific papers, we will also feature two shared tasks.


The first is a shared translation task which will examine translation between the following language pairs:

Participants may submit translations for any or all of the language directions. In addition to the common test sets the workshop organizers will provide optional training resources, including a newly expanded release of the Europarl corpora and out-of-domain corpora.

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 task to manually judge some of the submitted translations.

A more detailed description of the shared translation task (including information about the test and training corpora, a freely available MT system, and a number of other resources) is available from We also provide a baseline machine translation system, whose performance matches the best systems from last year's shared task.


The second task is a shared evaluation task. Participants in this task will submit automatic evaluation metrics for machine translation, which will be assessed on their ability to:

Participants in the shared translation task will submit translation results for a set of a few thousand sentences. Their system outputs will be distributed to participants in the shared evaluation task along with the reference translations. The translations will be ranked with automatic evaluation metrics. We will measure the correlation of automatic evaluation metrics with the human judgments.

More details of the shared evaluation task (including submission formats and the collected manual evaluations from last year's workshop) is available from


Submissions will consist of regular full papers of max. 8 pages, formatted following the ACL 2008 guidelines. In addition, shared task participants will be invited to submit short papers (max. 4 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.


Regular paper submissions March 14, 2008

(shared translation task) Results submissions March 21, 2008
(shared evaluation task) Results submissions April 4, 2008
(both shared tasks) Short paper submissions April 4, 2008

Notification April 12, 2008
Camera-ready papers April 21, 2008


Chris Callison-Burch (Johns Hopkins University)
Philipp Koehn (University of Edinburgh)
Christof Monz (University of London)
Josh Schroeder (University of Edinburgh)
Cameron Shaw Fordyce


Speaker: Daniel Marcu (Language Weaver/ISI)

Title: "Searching Efficiently for Solutions in Large-Scale NLP Application"

Abstract: As the fields of natural language processing and machine learning mature, the gap between the mathematical equations that we write when we model a problem statistically and the manner in which we implement these equations in NLP applications is widening. This talk reviews some of the challenges that we face when searching for best solutions in large-scale statistical applications, such as machine translation, and the effect that the ignoring of these challenges is having on end-to-end results. It also presents recent developments that have the potential to impact positively a wide range of applications where parameter estimation and search are critical.


Lars Ahrenberg (Linköping University)
Yaser Al-Onaizan (IBM Research)
Oliver Bender (RWTH Aachen)
Chris Brockett (Microsoft Research)
Bill Byrne (Cambridge University)
Francisco Casacuberta (University of Valencia)
Colin Cherry (Microsoft Research)
Stephen Clark (Oxford University)
Trevor Cohn (Edinburgh University)
Mona Diab (Columbia University)
Hal Daume (University of Utah)
Chris Dyer (University of Maryland)
Andreas Eisele (University Saarbrücken)
Marcello Federico (ITC-IRST)
George Foster (Canada National Research Council)
Alex Fraser (University of Stuttgart)
Ulrich Germann (University of Toronto)
Nizar Habash (Columbia University)
Jan Hajic (Charles University)
Keith Hall (Google)
John Henderson (MITRE)
Rebecca Hwa (University of Pittsburgh)
Doug Jones (Lincoln Labs MIT)
Damianos Karakos (Johns Hopkins University)
Kevin Knight (ISI/University of Southern California)
Shankar Kumar (Google)
Philippe Langlais (University of Montreal)
Alon Lavie (Carnegie Melon University)
Adam Lopez (Edinburgh University)
Daniel Marcu (ISI/University of Southern California)
Lambert Mathias (Johns Hopkins University)
Arul Menezes (Microsoft Research)
Bob Moore (Microsoft Research)
Miles Osborne (University of Edinburgh)
Kay Peterson (NIST)
Mark Przybocki (NIST)
Chris Quirk (Microsoft Research)
Philip Resnik (University of Maryland)
Michel Simard (National Research Council Canada)
Libin Shen (BBN Technologies)
Wade Shen (Lincoln Labs MIT)
Eiichiro Sumita (NICT/ATR)
David Talbot (Edinburgh University)
Jörg Tiedemann (University of Groningen)
Christoph Tillmann (IBM Research)
Kristina Toutanova (Microsoft Research)
Nicola Ueffing (National Research Council Canada)
Clare Voss (Army Research Labs)
Taro Watanabe (NTT)
Dekai Wu (HKUST)
Richard Zens (Google)


For questions, comments, etc. please send email to

supported by the EuroMatrix project, P6-IST-5-034291-STP
funded by the European Commission under Framework Programme 6