Findings of the Covid-19 MLIA Machine Translation Task

November 14, 2022 ยท Declared Dead ยท ๐Ÿ› arXiv.org

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Authors Francisco Casacuberta, Alexandru Ceausu, Khalid Choukri, Miltos Deligiannis, Miguel Domingo, Mercedes Garcรญa-Martรญnez, Manuel Herranz, Guillaume Jacquet, Vassilis Papavassiliou, Stelios Piperidis, Prokopis Prokopidis, Dimitris Roussis, Marwa Hadj Salah arXiv ID 2211.07465 Category cs.CL: Computation & Language Citations 0 Venue arXiv.org Last Checked 6 months ago
Abstract
This work presents the results of the machine translation (MT) task from the Covid-19 MLIA @ Eval initiative, a community effort to improve the generation of MT systems focused on the current Covid-19 crisis. Nine teams took part in this event, which was divided in two rounds and involved seven different language pairs. Two different scenarios were considered: one in which only the provided data was allowed, and a second one in which the use of external resources was allowed. Overall, best approaches were based on multilingual models and transfer learning, with an emphasis on the importance of applying a cleaning process to the training data.
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