42 citations · 92 across the 16 of their papers we have counts for
16 papers · 1 filter
Speech Translation with Foundation Models and Optimal Transport: UPC at IWSLT23
Ioannis Tsiamas, Gerard I. Gállego, José A. R. Fonollosa +1
This paper describes the submission of the UPC Machine Translation group to the IWSLT 2023 Offline Speech Translation task. Our Speech Translation systems utilize foundation models…
SegAugment: Maximizing the Utility of Speech Translation Data with Segmentation-based Augmentations
Ioannis Tsiamas, José A. R. Fonollosa, Marta R. Costa-jussà
End-to-end Speech Translation is hindered by a lack of available data resources. While most of them are based on documents, a sentence-level version is available, which is however…
Efficient Speech Translation with Dynamic Latent Perceivers
Ioannis Tsiamas, Gerard I. Gállego, José A. R. Fonollosa +1
Transformers have been the dominant architecture for Speech Translation in recent years, achieving significant improvements in translation quality. Since speech signals are longer…
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…
Sparsely Factored Neural Machine Translation
Noe Casas, Jose A. R. Fonollosa, Marta R. Costa-jussà
The standard approach to incorporate linguistic information to neural machine translation systems consists in maintaining separate vocabularies for each of the annotated features t…
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…
Syntax-driven Iterative Expansion Language Models for Controllable Text Generation
Noe Casas, José A. R. Fonollosa, Marta R. Costa-jussà
The dominant language modeling paradigm handles text as a sequence of discrete tokens. While that approach can capture the latent structure of the text, it is inherently constraine…
Automatic Spanish Translation of the SQuAD Dataset for Multilingual Question Answering
Casimiro Pio Carrino, Marta R. Costa-jussà, José A. R. Fonollosa
Recently, multilingual question answering became a crucial research topic, and it is receiving increased interest in the NLP community. However, the unavailability of large-scale d…
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…
Joint Source-Target Self Attention with Locality Constraints
José A. R. Fonollosa, Noe Casas, Marta R. Costa-jussà
The dominant neural machine translation models are based on the encoder-decoder structure, and many of them rely on an unconstrained receptive field over source and target sequence…
Towards Interlingua Neural Machine Translation
Carlos Escolano, Marta R. Costa-jussà, José A. R. Fonollosa
Common intermediate language representation in neural machine translation can be used to extend bilingual to multilingual systems by incremental training. In this paper, we propose…
(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.…
Character-level Intra Attention Network for Natural Language Inference
Han Yang, Marta R. Costa-jussà, José A. R. Fonollosa
Natural language inference (NLI) is a central problem in language understanding. End-to-end artificial neural networks have reached state-of-the-art performance in NLI field recent…
Character-based Neural Machine Translation
Marta R. Costa-Jussà, José A. R. Fonollosa
Neural Machine Translation (MT) has reached state-of-the-art results. However, one of the main challenges that neural MT still faces is dealing with very large vocabularies and mor…