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 translators, users, developers, and researchers of this increasingly viable technology.
The EAMT invites entries for its ninth EAMT Best Thesis Award for a PhD or equivalent thesis on a topic related to machine translation.
Previous year winners can be found at http://www.eamt.org/best_thesis.php.
* Eligibility *
- have completed a PhD (or equivalent) thesis on a relevant topic in a European, Northern African or Middle Eastern institution within calendar year 2020, - have not previously won another international award for that thesis, and, - are members of the EAMT at the time of submission,
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, based on the EAMT 2020 program 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 does not need to 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-assistedtranslation: methods, software or 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 on the 5th of September 2021 and will receive a prize of €500, together with an inscribed certificate. The recipient of the award will be required to briefly present their research at EAMT 2022. 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 complimentary membership in the EAMT for 2021 and 2022.
* Submission *
Candidates will submit using EasyChair: https://easychair.org/conferences/?conf=eamt2021 (Submission type: Thesis Award), a single PDF file containing:
- 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 "The Anthony C. Clarke Award for the 2020 EAMT Best Thesis" and - acknowledge the right of the EAMT to publicize the granting of the award.
For this year's Best Thesis Award we are requiring candidates to be an individual EAMT member at the time of submission. For EAMT memberships, please visit: http://www.eamt.org/membership.php.
* Closing date *
Submission deadline: June 30, 2021, 23:59 CEST. Award notification: September 5, 2021.
-- Carolina Scarton Academic Fellow Department of Computer Science University of Sheffield http://staffwww.dcs.shef.ac.uk/people/C.Scarton/