Adaptive neural machine translation systems, able to incrementally update the underlying models under an online learning regime, have been proven to be useful to improve the efficiency of this workflow.
This makes historical documents to be hard to comprehend by contemporary people and, thus, limits their accessibility to scholars specialized in the time period in which a certain document was written.
no code implementations • 14 Nov 2022 • 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
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.
Once the user is satisfied with the system's hypothesis and validates it, the system adapts its model following an online learning strategy.
Modernization aims at breaking this language barrier by generating a new version of a historical document, written in the modern version of the document's original language.
A common use of machine translation in the industry is providing initial translation hypotheses, which are later supervised and post-edited by a human expert.
We introduce a demonstration of our system, which implements online learning for neural machine translation in a production environment.
Separating punctuation and splitting tokens into words or subwords has proven to be helpful to reduce vocabulary and increase the number of examples of each word, improving the translation quality.