Findings of the Covid-19 MLIA Machine Translation Task

Francisco Casacuberta; Alexandru Ceausu; Khalid Choukri; Miltos Deligiannis; Miguel Domingo; Mercedes García-Martínez; Manuel Herranz; Guillaume Jacquet; Vassilis Papavassiliou; Stelios Piperidis; Prokopis Prokopidis; Dimitris Roussis; Marwa Hadj Salah

arXiv · 2211.07465

Findings of the Covid-19 MLIA Machine Translation Task

Francisco Casacuberta·Alexandru Ceausu·Khalid Choukri·Miltos Deligiannis·Miguel Domingo·Mercedes García-Martínez·Manuel Herranz·Guillaume Jacquet·Vassilis Papavassiliou·Stelios Piperidis·Prokopis Prokopidis·Dimitris Roussis·Marwa Hadj Salah

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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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Francisco Casacuberta, Alexandru Ceausu, Khalid Choukri, Miltos Deligiannis, Miguel Domingo, Mercedes García-Martínez, Manuel Herranz, Guillaume Jacquet, Vassilis Papavassiliou, Stelios Piperidis, Prokopis Prokopidis, Dimitris Roussis, Marwa Hadj Salah. 2022-11-14. Findings of the Covid-19 MLIA Machine Translation Task. https://arxiv.org/abs/2211.07465

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