The increasing use of machine translation in the workflow of professional translators creates demand for machine translation technology that provides more interactive collaboration, learns from its errors and adapts to the translators' style and adapts the underlying machine translation system online to the specific needs of the translator for the given task.
The next generation of computer aided translation (CAT) tools has to move beyond the use of static machine translation for human post-editing into a much richer division of labor between man and machine that takes full advantage of man's understanding of content and machine's greater ability to quickly process large amounts of data.
On the other hand, these tools will allow a more friendly interaction between the human and the machine through the use of different modalities of interactions as speech, gaze tracking, e-pen, etc. Finally, all of these issues will lead to an increase of the productivity of the professional translators.
Such tools are in development in a number of research labs across the world, one example is the open source workbench developed by the EU-funded projects Matecat and Casmacat, led by the organizers of this workshop.
This workshop brings to together researchers in this nascent subfield of machine translation. The workshop will divide its schedule about equally between invited talks by leading researchers and paper presentations on more recent advances.
|9am||Measuring Translation Productivity Offline - |
Some commercial challenges and research opportunities apparent from the iOmegaT project
John Moran, Trinity College / CNGL
|9:30am||Mixed-initiative Human Language Translation|
Spence Green, Stanford
|10am||Online and Active Learning for Machine Translation and Computer-Assisted Translation|
Jesús González-Rubio, Universitat Politècnica de València
|11am||Translators, Machine Translation and Trust|
Michel Simard, National Research Council Canada
|11:30am||Learning from Post-Editing: Real Time Model Adaptation for Machine Translation|
Michael Denkowski, CMU
|12pm||User-Adaptative MT in the MateCat Tool|
Marcello Federico, Fondazione Bruno Kessler
|12:30pm||The Human Language Model|
Lane Schwartz, University of Illinois at Urbana-Champaign
Integrating Online and Active Learning in a Computer-Assisted Translation Workbench
Vicent Alabau, Jesús González-Rubio, Daniel Ortiz-Martínez, Germán Sanchis Trilles, Francisco Casacuberta, Mercedes García-Martínez, Bartolomé Mesa-Lao, Dan Cheung Petersen, Barbara Dragsted and Michael Carl
Towards a Combination of Online and Multitask Learning for MT Quality Estimation: a Preliminary Study
Predicting Post-Editor Profiles from the Translation Process
Optimized MT Online Learning in Computer Assisted Translation
Behind the Scenes in an Interactive Speech Translation System
Dynamic Phrase Tables for Machine Translation in an Interactive Post-editing Scenario
For questions, comments, etc. please send email to email@example.com.
Supported by the European Commision|
under the Matecat and CASMACAT projects (grants 287688 and 287576).