no code implementations • WS 2020 • Christine Basta, Marta R. Costa-juss{\`a}, Jos{\'e} A. R. Fonollosa
Gender bias negatively impacts many natural language processing applications, including machine translation (MT).
no code implementations • ACL 2020 • Noe Casas, Marta R. Costa-juss{\`a}, Jos{\'e} A. R. Fonollosa
In Neural Machine Translation, using word-level tokens leads to degradation in translation quality.
no code implementations • CL 2020 • Marta R. Costa-juss{\`a}, Cristina Espa{\~n}a-Bonet, Pascale Fung, Noah A. Smith
We introduce the Computational Linguistics special issue on Multilingual and Interlingual Semantic Representations for Natural Language Processing.
no code implementations • LREC 2020 • Marta R. Costa-juss{\`a}, Esther Gonz{\'a}lez, Asuncion Moreno, Eudald Cumalat
We compare classical machine learning techniques to the use of a more ad-vanced model: the contextual word embeddings in the particular case of classification of abusive short-texts for the Spanishlanguage.
no code implementations • LREC 2020 • Casimiro Pio Carrino, Marta R. Costa-juss{\`a}, Jos{\'e} A. R. Fonollosa
We then used this dataset to train Spanish QA systems by fine-tuning a Multilingual-BERT model.
no code implementations • WS 2019 • Felipe Alfaro, Marta R. Costa-juss{\`a}, Jos{\'e} A. R. Fonollosa
This paper explains the TALP-UPC participation for the Gendered Pronoun Resolution shared-task of the 1st ACL Workshop on Gender Bias for Natural Language Processing.
no code implementations • WS 2019 • Lo{\"\i}c Barrault, Ond{\v{r}}ej Bojar, Marta R. Costa-juss{\`a}, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Philipp Koehn, Shervin Malmasi, Christof Monz, Mathias M{\"u}ller, Santanu Pal, Matt Post, Marcos Zampieri
This paper presents the results of the premier shared task organized alongside the Conference on Machine Translation (WMT) 2019.
no code implementations • WS 2019 • Casimiro Pio Carrino, Bardia Rafieian, Marta R. Costa-juss{\`a}, Jos{\'e} A. R. Fonollosa
Our best-submitted system ranked 2nd and 3rd for Spanish-English and English-Spanish translation directions, respectively.
no code implementations • WS 2019 • Joel Escud{\'e} Font, Marta R. Costa-juss{\`a}
We take advantage of the fact that word embeddings are used in neural machine translation to propose a method to equalize gender biases in neural machine translation using these representations.
no code implementations • WS 2019 • Noe Casas, Jos{\'e} A. R. Fonollosa, Carlos Escolano, Christine Basta, Marta R. Costa-juss{\`a}
In this article, we describe the TALP-UPC research group participation in the WMT19 news translation shared task for Kazakh-English.
no code implementations • WS 2019 • Kellie Webster, Marta R. Costa-juss{\`a}, Christian Hardmeier, Will Radford
The 1st ACL workshop on Gender Bias in Natural Language Processing included a shared task on gendered ambiguous pronoun (GAP) resolution.
no code implementations • WS 2018 • Noe Casas, Carlos Escolano, Marta R. Costa-juss{\`a}, Jos{\'e} A. R. Fonollosa
In this article we describe the TALP-UPC research group participation in the WMT18 news shared translation task for Finnish-English and Estonian-English within the multi-lingual subtrack.
no code implementations • WS 2018 • Brian Tubay, Marta R. Costa-juss{\`a}
The Transformer architecture has become the state-of-the-art in Machine Translation.
no code implementations • WS 2017 • Marta R. Costa-juss{\`a}, Carlos Escolano, Jos{\'e} A. R. Fonollosa
This paper presents experiments comparing character-based and byte-based neural machine translation systems.
no code implementations • WS 2017 • Marta R. Costa-juss{\`a}
Given the recent appearance and popularity of neural MT, this paper analyzes the performance of this new approach compared to the well-established rule-based and phrase-based MT systems.
no code implementations • WS 2013 • Llu{\'\i}s Formiga, Marta R. Costa-juss{\`a}, Jos{\'e} B. Mari{\~n}o, Jos{\'e} A. R. Fonollosa, Alberto Barr{\'o}n-Cede{\~n}o, Llu{\'\i}s M{\`a}rquez
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.
no code implementations • LREC 2012 • Jordi Adell, Antonio Bonafonte, Antonio Cardenal, Marta R. Costa-juss{\`a}, Jos{\'e} A. R. Fonollosa, Asunci{\'o}n Moreno, Eva Navas, Eduardo R. Banga
The paper presents the tool functionality, the architecture, the digital library and provide some information about the technology involved in the fields of automatic speech recognition, statistical machine translation, text-to-speech synthesis and information retrieval.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +7
no code implementations • LREC 2012 • Maite Melero, Marta R. Costa-juss{\`a}, Judith Domingo, Montse Marquina, Mart{\'\i} Quixal
We present work in progress aiming to build tools for the normalization of User-Generated Content (UGC).
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).