no code implementations • EAMT 2020 • Felipe Sánchez-Martínez, Víctor M. Sánchez-Cartagena, Juan Antonio Pérez-Ortiz, Mikel L. Forcada, Miquel Esplà-Gomis, Andrew Secker, Susie Coleman, Julie Wall
This paper describes our approach to create a neural machine translation system to translate between English and Swahili (both directions) in the news domain, as well as the process we followed to crawl the necessary parallel corpora from the Internet.
no code implementations • AMTA 2016 • Daniel Torregrosa, Juan Antonio Pérez-Ortiz, Mikel Forcada
The objective of interactive translation prediction (ITP), a paradigm of computer-aided translation, is to assist professional translators by offering context-based computer-generated suggestions as they type.
no code implementations • MTSummit 2021 • Alexandra Birch, Barry Haddow, Antonio Valerio Miceli Barone, Jindrich Helcl, Jonas Waldendorf, Felipe Sánchez Martínez, Mikel Forcada, Víctor Sánchez Cartagena, Juan Antonio Pérez-Ortiz, Miquel Esplà-Gomis, Wilker Aziz, Lina Murady, Sevi Sariisik, Peggy van der Kreeft, Kay Macquarrie
We find that starting from an existing large model pre-trained on 50languages leads to far better BLEU scores than pretraining on one high-resource language pair with a smaller model.
no code implementations • TRITON 2021 • Gema Ramírez-Sánchez, Juan Antonio Pérez-Ortiz, Felipe Sánchez-Martínez, Caroline Rossi, Dorothy Kenny, Riccardo Superbo, Pilar Sánchez-Gijón, Olga Torres-Hostench
The MultiTraiNMT Erasmus+ project aims at developing an open innovative syllabus in neural machine translation (NMT) for language learners and translators as multilingual citizens.
2 code implementations • 11 Apr 2024 • Andrés Lou, Juan Antonio Pérez-Ortiz, Felipe Sánchez-Martínez, Víctor M. Sánchez-Cartagena
The Mayan languages comprise a language family with an ancient history, millions of speakers, and immense cultural value, that, nevertheless, remains severely underrepresented in terms of resources and global exposure.
no code implementations • 29 Jan 2024 • Víctor M. Sánchez-Cartagena, Juan Antonio Pérez-Ortiz, Felipe Sánchez-Martínez
The study covers eight language pairs, different training corpus sizes, two architectures, and three types of annotation: dummy tags (with no linguistic information at all), part-of-speech tags, and morpho-syntactic description tags, which consist of part of speech and morphological features.
1 code implementation • 29 Jan 2024 • Víctor M. Sánchez-Cartagena, Miquel Esplà-Gomis, Juan Antonio Pérez-Ortiz, Felipe Sánchez-Martínez
When the amount of parallel sentences available to train a neural machine translation is scarce, a common practice is to generate new synthetic training samples from them.
1 code implementation • 16 Jan 2024 • Miquel Esplà-Gomis, Víctor M. Sánchez-Cartagena, Juan Antonio Pérez-Ortiz, Felipe Sánchez-Martínez
The paper presents an automatic evaluation of these techniques on four language pairs that shows that our approach can successfully exploit monolingual texts in a TM-based CAT environment, increasing the amount of useful translation proposals, and that our neural model for estimating the post-editing effort enables the combination of translation proposals obtained from monolingual corpora and from TMs in the usual way.
1 code implementation • EMNLP 2021 • Víctor M. Sánchez-Cartagena, Miquel Esplà-Gomis, Juan Antonio Pérez-Ortiz, Felipe Sánchez-Martínez
Many DA approaches aim at expanding the support of the empirical data distribution by generating new sentence pairs that contain infrequent words, thus making it closer to the true data distribution of parallel sentences.
no code implementations • 3 Apr 2020 • Felipe Sánchez-Martínez, Juan Antonio Pérez-Ortiz, Rafael C. Carrasco
Translation models based on hierarchical phrase-based statistical machine translation (HSMT) have shown better performances than the non-hierarchical phrase-based counterparts for some language pairs.