The EAMT annually invites entries for the Anthony C Clarke award: EAMT Best Thesis Award, for a PhD or equivalent thesis on a topic related to machine translation.
Eligible researchers should:
- have completed a PhD (or equivalent) thesis on a relevant topic in a European, Northern African or Middle Eastern institution within the calendar year specified by the call; and
- have not previously won another international award for that thesis.
The call for researchers that completed a PhD thesis in 2020 is open.
Previous EAMT Best Thesis awardees
- 2020: Felix Stahlberg: â€œThe Roles of Language Models and Hierarchical Models in Neural Sequence-to-Sequence Predictionâ€ (University of Cambridge), supervised by Bill Byrne and with Phil Woodland as advisor.
- 2019: Longyue Wang: “Discourse-Aware Neural Machine Translation” (Dublin City University – now at Tencent AI Lab), supervised by Andy Way (Dublin City University) and Qun Liu (Dublin City University – now at Huawei Noah’s Ark Lab).
- 2018: Daniel Emilio Beck: “Gaussian Processes for Text Regression” (University of Sheffield), supervised by Lucia Specia (University of Sheffield, UK) and Trevor Cohn (University of Melboune, Australia).
- 2017: JosÃ© Guilherme Camargo de Souza: “Adaptive Quality Estimation for Machine Translation and Automatic Speech Recognition” (University of Trento and FBK, Italy), supervised by Matteo Negri, Marco Turchi and Marcello Federico.
- 2016: Fabienne Braune: “Decoding Strategies for Syntax-based Statistical Machine Translation” (LMU Munich, Germany), supervised by Andreas Maletti and Alexander Fraser.
- 2015: Christian Hardmeier: “Discourse in Statistical Machine Translation” (Uppsala University, Sweden), supervised by Joakim Nivre and JÃ¶rg Tiedemann.
- 2014: Gennadi Lembersky: “The Effect of Translationese on Statistical Machine Translation” (University of Haifa, Israel), supervised by Shuly Wintner.
- 2013: unassigned
- 2012: Abby Levenberg:”Stream-based Statistical Machine Translation” (University of Edinburgh, UK), supervised by Miles Osborne.