8 citations · 35 across the 14 of their papers we have counts for
15 papers
Promoting Generalized Cross-lingual Question Answering in Few-resource Scenarios via Self-knowledge Distillation
Casimiro Pio Carrino, Carlos Escolano, José A. R. Fonollosa
Despite substantial progress in multilingual extractive Question Answering (QA), models with high and uniformly distributed performance across languages remain challenging, especia…
ReSeTOX: Re-learning attention weights for toxicity mitigation in machine translation
Javier García Gilabert, Carlos Escolano, Marta R. Costa-Jussà
Our proposed method, ReSeTOX (REdo SEarch if TOXic), addresses the issue of Neural Machine Translation (NMT) generating translation outputs that contain toxic words not present in…
Toxicity in Multilingual Machine Translation at Scale
Marta R. Costa-jussà, Eric Smith, Christophe Ropers +4
Machine Translation systems can produce different types of errors, some of which are characterized as critical or catastrophic due to the specific negative impact that they can hav…
Towards Opening the Black Box of Neural Machine Translation: Source and Target Interpretations of the Transformer
Javier Ferrando, Gerard I. Gállego, Belen Alastruey +2
In Neural Machine Translation (NMT), each token prediction is conditioned on the source sentence and the target prefix (what has been previously translated at a decoding step). How…
A multi-task semi-supervised framework for Text2Graph & Graph2Text
Oriol Domingo, Marta R. Costa-jussà, Carlos Escolano
The Artificial Intelligence industry regularly develops applications that mostly rely on Knowledge Bases, a data repository about specific, or general, domains, usually represented…
End-to-End Speech Translation with Pre-trained Models and Adapters: UPC at IWSLT 2021
Gerard I. Gállego, Ioannis Tsiamas, Carlos Escolano +2
This paper describes the submission to the IWSLT 2021 offline speech translation task by the UPC Machine Translation group. The task consists of building a system capable of transl…
Gender Bias in Multilingual Neural Machine Translation: The Architecture Matters
Marta R. Costa-jussà, Carlos Escolano, Christine Basta +3
Multilingual Neural Machine Translation architectures mainly differ in the amount of sharing modules and parameters among languages. In this paper, and from an algorithmic perspect…
Enabling Zero-shot Multilingual Spoken Language Translation with Language-Specific Encoders and Decoders
Carlos Escolano, Marta R. Costa-jussà, José A. R. Fonollosa +1
Current end-to-end approaches to Spoken Language Translation (SLT) rely on limited training resources, especially for multilingual settings. On the other hand, Multilingual Neural…
Training Multilingual Machine Translation by Alternately Freezing Language-Specific Encoders-Decoders
Carlos Escolano, Marta R. Costa-jussà, José A. R. Fonollosa +1
We propose a modular architecture of language-specific encoder-decoders that constitutes a multilingual machine translation system that can be incrementally extended to new languag…
Multilingual Machine Translation: Closing the Gap between Shared and Language-specific Encoder-Decoders
Carlos Escolano, Marta R. Costa-jussà, José A. R. Fonollosa +1
State-of-the-art multilingual machine translation relies on a universal encoder-decoder, which requires retraining the entire system to add new languages. In this paper, we propose…
Enriching the Transformer with Linguistic Factors for Low-Resource Machine Translation
Jordi Armengol-Estapé, Marta R. Costa-jussà, Carlos Escolano
Introducing factors, that is to say, word features such as linguistic information referring to the source tokens, is known to improve the results of neural machine translation syst…
From Bilingual to Multilingual Neural Machine Translation by Incremental Training
Carlos Escolano, Marta R. Costa-Jussà, José A. R. Fonollosa
Multilingual Neural Machine Translation approaches are based on the use of task-specific models and the addition of one more language can only be done by retraining the whole syste…
Multilingual, Multi-scale and Multi-layer Visualization of Intermediate Representations
Carlos Escolano, Marta R. Costa-jussà, Elora Lacroux +1
The main alternatives nowadays to deal with sequences are Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN) architectures and the Transformer. In this context, R…
(Self-Attentive) Autoencoder-based Universal Language Representation for Machine Translation
Carlos Escolano, Marta R. Costa-jussà, José A. R. Fonollosa
Universal language representation is the holy grail in machine translation (MT). Thanks to the new neural MT approach, it seems that there are good perspectives towards this goal.…
Morphology Generation for Statistical Machine Translation using Deep Learning Techniques
Marta R. Costa-jussà, Carlos Escolano
Morphology in unbalanced languages remains a big challenge in the context of machine translation. In this paper, we propose to de-couple machine translation from morphology generat…