8 citations · 16 across the 6 of their papers we have counts for
10 papers
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…