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2015 EAMT Best Thesis Award

The European Association for Machine Translation (EAMT, http://www.eamt.org) is an organization that serves the growing community of people interested in MT and translation tools, including users, developers, and researchers of this increasingly viable technology.

The EAMT invites entries for its fifth EAMT Best Thesis Award for a PhD or equivalent thesis on a topic related to machine translation.

Eligibility

Researchers who

  • have completed a PhD (or equivalent) thesis on a relevant topic in a European, Northern African[1] or Middle Eastern[2] institution within calendar year 2015 and
  • have not previously won another international award for that thesis,
  • are invited to submit their theses to the EAMT for consideration.

Panel

The submissions will be judged by a panel of experts who will be specifically appointed as part of the EAMT 2016 programme committee and which will be ratified by the Executive Board of the EAMT.

Selection criteria

Each thesis will be judged according to how challenging the problem was, to how relevant the results are for machine translation as a field, and to the strength of their impact in terms of scientific publications.

Scope

The scope of the thesis need not be confined to a technical area, and applications are also invited from students who carried out their research into commercial and management aspects of machine translation.

Possible areas of research include:

  • development of machine translation or advanced computer-assisted translation: software and resources
  • machine translation for less-resourced languages
  • the use of these systems in professional environments (freelance translators, translation agencies, localisation, etc.)
  • the increasing impact of machine translation on non-professional Internet users and its impact in communications, social networking, etc.
  • spoken language translation
  • the integration of machine translation and translation memory systems
  • the integration of machine translation software in larger IT applications
  • the evaluation of machine translation systems in real tasks such as those above
  • the cross-fertilisation between machine translation and other language technologies

Prize

The winner will be announced at the same time as accepted papers for the 19th Annual Conference of the European Association for Machine Translation EAMT 2016 (Riga, Latvia, dates still to be announced), and will receive a prize of €500, together with a suitably-inscribed certificate. The recipient of the award will be required to briefly present their research at EAMT 2016. In order to facilitate this, the EAMT will waive the winner's registration costs, and will make available a travel bursary of €200 to enable the recipient of the award to attend the said conference. The prize includes complmentary membership in the EAMT for 2016 and 2017.

Submission

Candidates will submit using EasyChair: https://easychair.org/conferences/?conf=eamt2016, a single PDF file containing, in this order:

  • a 2-page summary of your thesis in English, containing:
    • your full contact details,
    • the name and contact details of your supervisor(s),
  • a copy of your CV in English (at most one page, plus a complete list of publications directly related to the thesis)
  • an electronic copy of your thesis
  • optionally, an appendix with any other relevant information on the thesis

By submitting their work, authors

  • agree that, in case they are granted the award, any subsequently published version of the thesis should carry the citation "Winner of the 2015 European Association for Machine Translation Best Thesis Award" and
  • acknowledge the right of the EAMT to publicize the granting of the award.

Closing date

The closing date for submissions will be the same as the deadline for research papers at EAMT 2016, which will also be announced in autumn of 2015.

[1] Algeria, Egypt, Libya, Morocco and Tunisia.
[2] Bahrain, Iran, Iraq, Israel, Jordan, Kuwait, Oman, Palestine, Qatar, Saudi Arabia, Syria, United Arab Emirates, Yemen.