Search Results for author: Miguel Domingo

Found 10 papers, 2 papers with code

A User Study of the Incremental Learning in NMT

no code implementations EAMT 2020 Miguel Domingo, Mercedes García-Martínez, Álvaro Peris, Alexandre Helle, Amando Estela, Laurent Bié, Francisco Casacuberta, Manuel Herranz

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.

Incremental Learning Machine Translation +2

A Machine Translation Approach for Modernizing Historical Documents Using Backtranslation

no code implementations IWSLT (EMNLP) 2018 Miguel Domingo, Francisco Casacuberta

This makes historical documents to be hard to comprehend by contemporary people and, thus, limits their accessibility to scholars specialized in the time period in which a certain document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.

Machine Translation Translation

Findings of the Covid-19 MLIA Machine Translation Task

no code implementations14 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.

Machine Translation Transfer Learning +1

Two Demonstrations of the Machine Translation Applications to Historical Documents

1 code implementation2 Feb 2021 Miguel Domingo, Francisco Casacuberta

Once the user is satisfied with the system's hypothesis and validates it, the system adapts its model following an online learning strategy.

Machine Translation Translation +1

An Interactive Machine Translation Framework for Modernizing Historical Documents

no code implementations8 Oct 2019 Miguel Domingo, Francisco Casacuberta

Modernization aims at breaking this language barrier by generating a new version of a historical document, written in the modern version of the document's original language.

Machine Translation Translation

Modernizing Historical Documents: a User Study

no code implementations1 Jul 2019 Miguel Domingo, Francisco Casacuberta

This is due to the language barrier inherent in human language and the linguistic properties of these documents.

Machine Translation Translation

How Much Does Tokenization Affect Neural Machine Translation?

no code implementations20 Dec 2018 Miguel Domingo, Mercedes Garcıa-Martınez, Alexandre Helle, Francisco Casacuberta, Manuel Herranz

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.

Machine Translation NMT +2

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