Search Results for author: Maite Melero

Found 20 papers, 4 papers with code

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.


Spanish Datasets for Sensitive Entity Detection in the Legal Domain

no code implementations LREC 2022 Ona de Gibert Bonet, Aitor García Pablos, Montse Cuadros, Maite Melero

In order to assess the quality of the generated datasets, we have used them to fine-tune a battery of entity-detection models, using as foundation different pre-trained language models: one multilingual, two general-domain monolingual and one in-domain monolingual.


Unsupervised Machine Translation in Real-World Scenarios

no code implementations LREC 2022 Ona de Gibert Bonet, Iakes Goenaga, Jordi Armengol-Estapé, Olatz Perez-de-Viñaspre, Carla Parra Escartín, Marina Sanchez, Mārcis Pinnis, Gorka Labaka, Maite Melero

In this work, we present the work that has been carried on in the MT4All CEF project and the resources that it has generated by leveraging recent research carried out in the field of unsupervised learning.

Translation Unsupervised Machine Translation

Sequence-to-Sequence Resources for Catalan

1 code implementation14 Feb 2022 Ona de Gibert, Ksenia Kharitonova, Blanca Calvo Figueras, Jordi Armengol-Estapé, Maite Melero

In this work, we introduce sequence-to-sequence language resources for Catalan, a moderately under-resourced language, towards two tasks, namely: Summarization and Machine Translation (MT).

Abstractive Text Summarization Machine Translation +1

The Catalan Language CLUB

no code implementations3 Dec 2021 Carlos Rodriguez-Penagos, Carme Armentano-Oller, Marta Villegas, Maite Melero, Aitor Gonzalez, Ona de Gibert Bonet, Casimiro Carrino Pio

The Catalan Language Understanding Benchmark (CLUB) encompasses various datasets representative of different NLU tasks that enable accurate evaluations of language models, following the General Language Understanding Evaluation (GLUE) example.

Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? A Comprehensive Assessment for Catalan

no code implementations Findings (ACL) 2021 Jordi Armengol-Estapé, Casimiro Pio Carrino, Carlos Rodriguez-Penagos, Ona de Gibert Bonet, Carme Armentano-Oller, Aitor Gonzalez-Agirre, Maite Melero, Marta Villegas

For this, we: (1) build a clean, high-quality textual Catalan corpus (CaText), the largest to date (but only a fraction of the usual size of the previous work in monolingual language models), (2) train a Transformer-based language model for Catalan (BERTa), and (3) devise a thorough evaluation in a diversity of settings, comprising a complete array of downstream tasks, namely, Part of Speech Tagging, Named Entity Recognition and Classification, Text Classification, Question Answering, and Semantic Textual Similarity, with most of the corresponding datasets being created ex novo.

Language Modelling named-entity-recognition +7

English-Catalan Neural Machine Translation in the Biomedical Domain through the cascade approach

no code implementations19 Mar 2018 Marta R. Costa-jussà, Noe Casas, Maite Melero

This paper describes the methodology followed to build a neural machine translation system in the biomedical domain for the English-Catalan language pair.

Machine Translation Translation

Leveraging RDF Graphs for Crossing Multiple Bilingual Dictionaries

1 code implementation LREC 2016 Marta Villegas, Maite Melero, N{\'u}ria Bel, Jorge Gracia

The experiments presented here exploit the properties of the Apertium RDF Graph, principally cycle density and nodes{'} degree, to automatically generate new translation relations between words, and therefore to enrich existing bilingual dictionaries with new entries.


A Richly Annotated, Multilingual Parallel Corpus for Hybrid Machine Translation

no code implementations LREC 2012 Eleftherios Avramidis, Marta R. Costa-juss{\`a}, Christian Federmann, Josef van Genabith, Maite Melero, Pavel Pecina

This corpus aims to serve as a basic resource for further research on whether hybrid machine translation algorithms and system combination techniques can benefit from additional (linguistically motivated, decoding, and runtime) information provided by the different systems involved.

Machine Translation Translation

The ML4HMT Workshop on Optimising the Division of Labour in Hybrid Machine Translation

no code implementations LREC 2012 Christian Federmann, Eleftherios Avramidis, Marta R. Costa-juss{\`a}, Josef van Genabith, Maite Melero, Pavel Pecina

We describe the “Shared Task on Applying Machine Learning Techniques to Optimise the Division of Labour in Hybrid Machine Translation” (ML4HMT) which aims to foster research on improved system combination approaches for machine translation (MT).

Language Modelling Machine Translation +1

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