The Anthony C. Clarke Award for the 2023 EAMT Best Thesis

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 twelfth EAMT Best Thesis Award for a PhD or equivalent thesis on a topic related to machine translation. 

Previous year winners can be found at https://eamt.org/best-thesis-award/. 

Eligibility

Researchers who

• have completed a PhD (or equivalent) thesis on a relevant topic in a European, African or Middle Eastern institution within calendar year 2023,
• 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 2024 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-assisted translation: 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 8th of March 2024 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 2024 to be held from 24th June to 27th June 2024 in Sheffield, UK. 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 2025.

Submission

Candidates will submit, using OpenReview (https://openreview.net/group?id=EAMT.org/2024/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),
  • the main aspects of your work, namely goal/objectives, methodology and results;

• 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 2023 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: https://eamt.org/101-2/.

Closing date

• Submission deadline: February 8, 2024, 23:59 CEST.
• Award notification: March 8, 2024.

2022 Anthony C Clarke Award for the EAMT Best Thesis: awardee announcement

Nine PhD theses defended in 2022 were received as candidates for the 2022 edition of the EAMT Best Thesis Award, and all nine were eligible. 28 reviewers and six EAMT Executive Committee members were recruited to examine and score the theses, considering how challenging the problem tackled in each thesis was, how relevant the results were for machine translation as a field, and what the strength of its impact in terms of scientific publications was. Two EAMT Executive Committee members also analysed all theses. It became very clear that 2022 was another very good year for PhD theses in machine translation. 

All theses had merit, all candidates had strong CVs and, therefore, it was very difficult to select a winner.

A panel of two EAMT Executive Committee members (Carolina Scarton and Helena Moniz) was assembled to process the reviews and select a winner that was later ratified by the EAMT executive committee.

We are pleased to announce that the awardee of the 2022 edition of the EAMT Best Thesis is Biao Zhang’s thesis “Towards Efficient Universal Neural Machine Translation” (University of Edinburgh, UK), supervised by Dr Rico Sennrich and Dr Ivan Titov.

The awardee will receive a prize of €500, together with a suitably-inscribed certificate. In addition, Dr. Zhang will present a summary of their thesis at the 24th Annual Conference of the European Association for Machine Translation (EAMT 2023: https://events.tuni.fi/eamt23/) which will take place from June 12th to 15th in Tampere, Finland. In order to facilitate this, the EAMT will waive the winner’s registration costs, and will make available a travel bursary of €200.

Helena Moniz, EAMT President

Carolina Scarton, EAMT Secretary

Program committee 

Rachel Bawden, Inria

Daniel Beck, The University of Melbourne

William Byrne, University of Cambridge

José G. C. de Souza, Unbabel

Vera Cabarrão, Unbabel / INESC-ID

Sheila Castilho, Dublin City University

Anna Currey, Amazon Web Services

Mattia Antonino Di Gangi, AppTek

Maha Elbayad, LIG/ Inria

Miquel Esplà-Gomis, Universitat d’Alacant

Marcello Federico, Amazon AI

Mikel Forcada, DLSI – Universitat d’Alacant

Barry Haddow, The University of Edinburgh

Diptesh Kanojia, IIT Bombay

Philipp Koehn, Johns Hopkins University

Helena Moniz, INESC/FLUL

Mary Nurminen, Tampere University

Constantin Orasan, University of Surrey

John E. Ortega, Northeastern University

Santanu Pal, Wipro Limited

Pavel Pecina, Charles University

Maja Popovic, ADAPT Centre @ DCU

Celia Rico, Universidad Complutense de Madrid

Víctor M. Sánchez-Cartagena, Universitat d’Alacant

Marina Sánchez-Torrón, Unbabel

Danielle Saunders, University of Cambridge

Dimitar Shterionov, Tilburg University

Felix Stahlberg, Google Research

Arda Tezcan, Ghent University

Antonio Toral, University of Groningen

Ualsher Tukeyev, al-Farabi Kazakh National University

Bram Vanroy, KU Leuven; Ghent University

Longyue Wang, Tencent AI Lab

The Anthony C. Clarke Award for the 2022 EAMT Best Thesis

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 eleventh EAMT Best Thesis Award for a PhD or equivalent thesis on a topic related to machine translation. 

Previous year winners can be found at https://eamt.org/best-thesis-award/. 

Eligibility

Researchers who

• have completed a PhD (or equivalent) thesis on a relevant topic in a European, African or Middle Eastern (1) institution within calendar year 2022,
• 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. 

(1)  Bahrain, Iran, Iraq, Israel, Jordan, Kuwait, Oman, Palestine, Qatar, Saudi Arabia, Syria, United Arab Emirates, Yemen.

Panel

The submissions will be judged by a panel of experts who will be specifically appointed, based on the EAMT 2023 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-assisted translation: 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 31st of March 2023 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 2023 to be held from 12th June to 15th June 2022 in Tampere, Finland. 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 2024.

Submission

Candidates will submit using EasyChair: https://easychair.org/conferences/?conf=eamt2023 (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 2022 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: March 3 March 10, 2023, 23:59 CEST.
• Award notification: March 31 April 6, 2023.

2021 Anthony C Clarke Award for the EAMT Best Thesis: awardee announcement

Six PhD theses defended in 2021 were received as candidates for the 2021 edition of the Anthony C Clarke Award for the EAMT Best Thesis, and all six were eligible. 12 reviewers and five EAMT Executive Committee members were recruited to examine and score the theses, considering how challenging the problem tackled in each thesis was, how relevant the results were for machine translation as a field, and what the strength of its impact in terms of scientific publications was. Two EAMT Executive Committee members (Helena Moniz – EAMT President – and Carolina Scarton – EAMT Secretary) formed a panel to analyse all theses and discuss all reviews.

The year of 2021 was again a very good year for PhD theses in machine translation. The scores of the best theses were very close, which made it very hard to select a winner. After discussing all the theses and their reviews, the panel proposed a winner that was approved by the EAMT executive committee. 

We are pleased to announce that the awardee of the 2021 edition of the EAMT Best Thesis is Danielle Saunders’ thesis “Domain Adaptation for Neural Machine Translation” (University of Cambridge, UK – now at RWS Language Weaver), supervised by Professor Bill Byrne.

The awardee will receive a prize of €500, together with a suitably-inscribed certificate. In addition, Dr. Saunders will present a summary of their thesis at the 23rd Annual Conference of the European Association for Machine Translation (EAMT 2022: https://eamt2022.com) which will take place from June 1st to 3rd in Ghent, Belgium. In order to facilitate this, the EAMT will waive the winner’s registration costs, and will make available a travel bursary of €200.

Helena Moniz, EAMT President
Carolina Scarton, EAMT Secretary

Program committee

José G. C. de Souza, Unbabel
Vera Cabarrão, Unbabel / INESC-ID
Sheila Castilho, DCU
Mattia Antonino Di Gangi, AppTek
Mirella De Sisto, Tilburg University
Miquel Esplà-Gomis, Universitat d’Alacant
Federico Gaspari, DCU
Barry Haddow, University of Edinburgh
Ekaterina Lapshinova-Koltunski, Saarland University
Maja Popovic, DCU
Lieve Macken, Ghent University
André Martins, Unbabel
Celia Rico, Spain
Víctor M. Sánchez-Cartagena, Universitat d’Alacant
Marina Sánchez-Torrón, Unbabel
Arda Tezcan, Ghent University
Marco Turchi, Fondazione Bruno Kessler

The Anthony C. Clarke Award for the 2021 EAMT Best Thesis

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 tenth EAMT Best Thesis Award for a PhD or equivalent thesis on a topic related to machine translation. 

Previous year winners can be found at https://eamt.org/best-thesis-award/. 

Eligibility

Researchers who

• have completed a PhD (or equivalent) thesis on a relevant topic in a European, Northern African or Middle Eastern institution within calendar year 2021,
• 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 2022 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-assisted translation: 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 30th of April 2022 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 to be held from 1st June to 3rd June 2022 in Ghent, Belgium. 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 2022 and 2023.

Submission

Candidates will submit using EasyChair: https://easychair.org/conferences/?conf=eamt2022 (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 2021 EAMT Best Thesis” and
• acknowledge the right of the EAMT to publicize the granting of the award.

For this year 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: April 1, 2022, 23:59 CEST.
• Award notification: April 30, 2022.

2020 EAMT Best Thesis Award winners

Four PhD theses defended in 2020 were received as candidates for the 2020 edition of the Anthony C Clarke Award – EAMT Best Thesis Award, and all four were eligible. Eight EAMT Executive Committee members were recruited to examine and score the theses, considering how challenging the problem tackled in each thesis was, how relevant the results were for machine translation as a field, and what the strength of its impact in terms of scientific publications was. Two EAMT Executive Committee members also analysed all theses.

The scores of the best theses were extremely close, which made it very hard to select a single winner. A panel of seven EAMT Executive Committee members (Khalil Sima’an, Barry Haddow, Celia Rico, Lieve Macken, Carolina Scarton, Helena Moniz and Mikel L. Forcada) was assembled to process and discuss the reviews.

After a lot of consideration, the panel has decided to have two ex aequo winners for the 2020 edition of the EAMT Best Thesis Award:

• Maha Elbayad: Rethinking the Design of Sequence-to-Sequence Models for Efficient Machine Translation (University Grenoble Alpes, France) — supervised by Laurent Besacier and Jakob Verbeek
• Mattia Antonino Di Gangi: Neural Speech Translation: From Neural Machine Translation to Direct Speech Translation (University of Trento, Italy) — supervised by Marcello Federico, Marco Turchi and Matteo Negri

The awardees will receive a prize of €500, together with a suitably-inscribed certificate. In addition, Dr. Elbayad and Dr. Di Gangi have been invited to present a summary of their theses at the 23nd Annual Conference of the European Association for Machine Translation (EAMT 2022: https://eamt2022.com) which will take place in June, 1-3 2021 .

Helena Moniz, EAMT President
Carolina Scarton, EAMT Secretary

2019 EAMT Best Thesis Awardee

Ten PhD theses defended in 2019 were received as candidates for the 2019 edition of the Anthony C Clarke Award – EAMT Best Thesis Award, and all ten were eligible. 36 reviewers and six EAMT Executive Committee members were recruited to examine and score the theses, considering how challenging the problem tackled in each thesis was, how relevant the results were for machine translation as a field, and what the strength of its impact in terms of scientific publications was. Two EAMT Executive Committee members also analysed all theses.

The year of 2019 was again a very good year for PhD theses in machine translation. The scores of the best theses were very close, which made it very hard to select a winner. A panel of five EAMT Executive Committee members (André Martins, Lucia Specia, Khalil Sima’an, Carolina Scarton, and Mikel L. Forcada) was assembled to process the reviews and select a winner.

The panel has then decided to grant the 2019 edition of the EAMT Best Thesis Award to Felix Stahlberg’s thesis “The Roles of Language Models and Hierarchical Models in Neural Sequence-to-Sequence Prediction” (University of Cambridge — now at Google), supervised by Bill Byrne and with Phil Woodland as advisor.

The awardee will receive a prize of €500, together with a suitably-inscribed certificate. In addition, Dr. Stahlberg has been invited to present a summary of his thesis at the 22nd Annual Conference of the European Association for Machine Translation (EAMT 2020: https://eamt2020.inesc-id.pt) which will take place in November (dates to be confirmed).

Mikel L. Forcada

EAMT President

Carolina Scarton

EAMT Secretary

Program committee

We would like to thank our reviewers that, despite the COVID-19 pandemic, still gave their time to review the theses and help us select the winner. Specially, Julia Ive, Iacer Calixto, José G. C. de Souza, and Jesús González Rubio that also acted as emergency reviewers.

• Alon Lavie, Carnegie Mellon University
• Andreas Maletti, Universität Leipzig
• André Martins, Unbabel
• Andy Way, Dublin City University
• Antonio Toral, University of Groningen
• Barry Haddow, University of Edinburgh
• Celia Rico, Universidad Europea de Madrid
• Christian Hardmeier, Uppsala University
• Cristina España-Bonet, UdS and DFKI
• Daniel Beck, University of Melbourne
• Dimitar Shterionov, Dublin City University
• Ekaterina Lapshinova-Koltunsk, Saarland University
• Federico Gaspari, Dublin City University
• Francisco Casacuberta, Universitat Politècnica de València
• François Yvon, Université Paris-Sud
• Frederic Blain, University of Sheffield
• Iacer Calixto, University of Amsterdam
• Jan Niehues, Maastricht University
• Jesús González Rubio, WebInterpret
• Joke Daems, Ghent University
• José G. C. de Souza, Unbabel
• Julia Ive, Imperial College London
• Lieve Macken, Ghent University
• Loïc Barrault, University of Sheffield
• Longyue Wang, Tencent AI Lab
• Lucia Specia, Imperial College London
• Maja Popovic, Dublin City University
• Marcello Federico, Amazon AI
• Markus Freitag, Google AI
• Matteo Negri, Fondazione Bruno Kessler
• Mauro Cettolo, Fondazione Bruno Kessler
• Miguel Domingo, Universitat Politècnica de València
• Miquel Esplà, Universitat d’Alacant
• Patrick Simianer, Lilt Inc.
• Pavel Pecina, Charles University
• Philipp Koehn, Johns Hopkins University
• Qun Liu, Huawei Noah’s Ark Lab
• Rico Sennrich, University of Zurich
• Sharon O’Brien, Dublin City University
• Sheila Castilho, Dublin City University
• Vincent Vandeghinste, KU Leuven