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