Search Results for author: Manuel Herranz

Found 14 papers, 0 papers with code

English-Russian Data Augmentation for Neural Machine Translation

no code implementations AMTA 2022 Nikita Teslenko Grygoryev, Mercedes Garcia Martinez, Francisco Casacuberta Nolla, Amando Estela Pastor, Manuel Herranz

In order to evaluate the quality of the NMT models, firstly, these models have been compared performing a quantitative analysis by means of several standard automatic metrics used in machine translation, and measuring the time spent and the amount of text generated for a good use in the language industry.

Data Augmentation Machine Translation +2

MAPA Project: Ready-to-Go Open-Source Datasets and Deep Learning Technology to Remove Identifying Information from Text Documents

no code implementations LEGAL (LREC) 2022 Victoria Arranz, Khalid Choukri, Montse Cuadros, Aitor García Pablos, Lucie Gianola, Cyril Grouin, Manuel Herranz, Patrick Paroubek, Pierre Zweigenbaum

This paper presents the outcomes of the MAPA project, a set of annotated corpora for 24 languages of the European Union and an open-source customisable toolkit able to detect and substitute sensitive information in text documents from any domain, using state-of-the art, deep learning-based named entity recognition techniques.

De-identification named-entity-recognition +2

Europeana Translate: Providing multilingual access to digital cultural heritage

no code implementations EAMT 2022 Eirini Kaldeli, Mercedes García-Martínez, Antoine Isaac, Paolo Sebastiano Scalia, Arne Stabenau, Iván Lena Almor, Carmen Grau Lacal, Martín Barroso Ordóñez, Amando Estela, Manuel Herranz

Europeana Translate is a project funded under the Connecting European Facility with the objective to take advantage of state-of-the-art machine translation in order to increase the multilinguality of resources in the cultural heritage domain

Machine Translation Translation

Neural Translation for the European Union (NTEU) Project

no code implementations EAMT 2020 Laurent Bié, Aleix Cerdà-i-Cucó, Hans Degroote, Amando Estela, Mercedes García-Martínez, Manuel Herranz, Alejandro Kohan, Maite Melero, Tony O’Dowd, Sinéad O’Gorman, Mārcis Pinnis, Roberts Rozis, Riccardo Superbo, Artūrs Vasiļevskis

The Neural Translation for the European Union (NTEU) project aims to build a neural engine farm with all European official language combinations for eTranslation, without the necessity to use a high-resourced language as a pivot.


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

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

Eco.pangeamt: Industrializing Neural MT

no code implementations LREC 2020 Mercedes Garc{\'\i}a-Mart{\'\i}nez, Manuel Herranz, Am Estela, o, {\'A}ngela Franco, Laurent Bi{\'e}

Eco is Pangeanic{'}s customer portal for generic or specialized translation services (machine translation and post-editing, generic API MT and custom API MT).

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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