For a Human-Centered AI

Mattia Antonino Di Gangi’s excellence path in machine translation awarded

October 15, 2021

The Best Thesis 2020 award of the EAMT European association went to the former doctoral student of the machine translation team at Fondazione Bruno Kessler

Mattia Antonino Di Gangi, a former doctoral student with the machine translation team at Fondazione Bruno Kessler, received the prize, ex aequo with another candidate, for the best European doctoral thesis on machine translation. The award was announced by the European machine translation association (EAMT) which selected the theses and evaluated them through a committee of experts: Khalil Sima’an, Barry Haddow, Celia Rico, Lieve Macken, Carolina Scarton, Helena Moniz and Mikel L. Forcada.

Mattia Antonino di Gangi’s work at Fondazione Bruno Kessler was supervised by Marcello Federico, Marco Turchi and Matteo Negri and the recognition awards excellence pursued during his doctorate in a domain, machine translation, in which FBK’s excellence is recognized worldwide.

Mattia Antonino di Gangi’s thesis entitled “Neural Speech Translation: From Neural Machine Translation to Direct Speech Translation” was awarded together with that of Maha Elbayad (France).

“We are very happy”, Marco Turchi (FBK) said, “for the award received by Mattia, which represents an important recognition for a very high quality thesis. His work has contributed to push forward a real revolution in the world of speech translation, namely the transition from so-called cascade systems to direct ones. The publications that led to the thesis have created new conditions for this change, making new data and advanced technology available to the scientific and industrial community. The award ends three years of hard work in the best possible way”

It is not the first time that a former FBK PhD student has achieved this recognition. In 2017, the prize was awarded to José Camargo De Souza for his thesis on “Adaptive Quality Prediction for Machine Translation and Speech Recognition”.

 


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