Recent Submissions

  • Attention weights in transformer NMT fail aligning words between sequences but largely explain model predictions 

    Ferrando Monsonís, Javier; Ruiz Costa-Jussà, Marta (Association for Computational Linguistics, 2021)
    Conference lecture
    Open Access
    This work proposes an extensive analysis of the Transformer architecture in the Neural Machine Translation (NMT) setting. Focusing on the encoder-decoder attention mechanism, we prove that attention weights systematically ...
  • On the locality of attention in direct speech translation 

    Alastruey Lasheras, Belén; Ferrando Monsonís, Javier; Gallego Olsina, Gerard Ion; Ruiz Costa-Jussà, Marta (Association for Computational Linguistics, 2022)
    Conference lecture
    Open Access
    Transformers have achieved state-of-the-art results across multiple NLP tasks. However, the self-attention mechanism complexity scales quadratically with the sequence length, creating an obstacle for tasks involving long ...
  • High frequent in-domain word segmentation and forward translation for the WMT21 Biomedical task 

    Rafieian, Bardia; Ruiz Costa-Jussà, Marta (Association for Computational Linguistics, 2021)
    Conference report
    Open Access
    This paper reports the optimization of using the out-of-domain data in the Biomedical translation task. We firstly optimized our parallel training dataset using the BabelNet in-domain terminology words. Afterward, to ...
  • Enhancing sequence-to-sequence modeling for RDF triples to natural text 

    Domingo Roig, Oriol; Bergés Lladó, David; Cantenys Sabà, Roser; Creus Castanyer, Roger; Rodríguez Fonollosa, José Adrián (Association for Computational Linguistics, 2020)
    Conference report
    Open Access
    Establishes key guidelines on how, which and when Machine Translation (MT) techniques are worth applying to RDF-to-Text task. Not only do we apply and compare the most prominent MT architecture, the Transformer, but we ...
  • The UPC RDF-to-Text System at WebNLG Challenge 2020 

    Bergés Lladó, David; Cantenys Sabà, Roser; Creus Castanyer, Roger; Domingo Roig, Oriol; Rodríguez Fonollosa, José Adrián (Association for Computational Linguistics, 2020)
    Conference report
    Open Access
    This work describes the end-to-end system architecture presented at WebNLG Challenge 2020. The system follows the traditional Machine Translation (MT) pipeline, based on the Transformer model, applied in most text-to-text ...
  • The TALP-UPC participation in WMT21 news translation task: an mBART-based NMT approach 

    Escolano Peinado, Carlos; Tsiamas, Ioannis; Basta, Christine Raouf Saad; Ferrando Monsonís, Javier; Ruiz Costa-Jussà, Marta; Rodríguez Fonollosa, José Adrián (Association for Computational Linguistics, 2021)
    Conference report
    Open Access
    This paper describes the submission to the WMT 2021 news translation shared task by the UPC Machine Translation group. The goal of the task is to translate German to French (De-Fr) and French to German (Fr-De). Our submission ...
  • Enriching the transformer with linguistic factors for low-resource machine translation 

    Armengol Estapé, Jordi; Ruiz Costa-Jussà, Marta; Escolano Peinado, Carlos (Incoma, Ltd., 2021)
    Conference report
    Open Access
    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 systems in certain settings, typically in ...
  • Self-supervised deep learning approaches to speaker recognition: A Ph.D. Thesis overview 

    Khan, Umair; Hernando Pericás, Francisco Javier (International Speech Communication Association (ISCA), 2021)
    Conference lecture
    Open Access
    Recent advances in Deep Learning (DL) for speaker recognition have improved the performance but are constrained to the need of labels for the background data, which is difficult in prac- tice. In i-vector based speaker ...
  • End-to-end speech translation with pre-trained models and adapters: UPC at IWSLT 2021 

    Gallego Olsina, Gerard Ion; Tsiamas, Ioannis; Escolano Peinado, Carlos; Rodríguez Fonollosa, José Adrián; Ruiz Costa-Jussà, Marta (Association for Computational Linguistics, 2021)
    Conference report
    Open Access
    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 translating English audio recordings extracted ...
  • Soporte universitario al paso a docencia virtual debido a la COVID-19 y su seguimiento por parte del profesorado 

    Amante García, Beatriz; Macarulla Martí, Marcel; Canals Casals, Lluc; Tejedor Herrán, Blanca; Vallverdú Bayés, Sisco (Asociación Española de Ingeniería de Proyectos (AEIPRO), 2021)
    Conference report
    Open Access
    La virtualidad de las clases universitarias es una realidad imperativa desde el primer confinamiento provocado por la COVID-19. Cada universidad ha tenido un proceso de adaptación diferente y en grandes universidades con ...
  • Double multi-head attention for speaker verification 

    India Massana, Miquel Àngel; Safari, Pooyan; Hernando Pericás, Francisco Javier (Institute of Electrical and Electronics Engineers (IEEE), 2021)
    Conference report
    Open Access
    Most state-of-the-art Deep Learning systems for text-independent speaker verification are based on speaker embedding extractors. These architectures are commonly composed of a feature extractor front-end together with a ...
  • Multilingual machine translation: Closing the gap between shared and language-specific encoder-decoders 

    Escolano Peinado, Carlos; Ruiz Costa-Jussà, Marta; Rodríguez Fonollosa, José Adrián; Artetxe Zurutuza, Mikel (Association for Computational Linguistics, 2021)
    Conference lecture
    Open Access
    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 an alternative approach that is based on ...

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