Marta R. Costa‐jussà
10 papers in the PaperMetrix corpus
Papers by this author
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(Self-Attentive) Autoencoder-based Universal Language Representation for Machine Translation
2018 · arXiv (Cornell University)
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. In this paper, we propose a new …
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From bilingual to multilingual neural‐based machine translation by incremental training
2020 · Journal of the Association for Information Science and Technology
Abstract A common intermediate language representation in neural machine translation can be used to extend bilingual systems by incremental training. We propose a new architecture based on introducing an interlingual loss as an additional training …
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Findings of the First Shared Task on Lifelong Learning Machine Translation
2020
A lifelong learning system can adapt to new data without forgetting previously acquired knowledge. In this paper, we introduce the first benchmark for lifelong learning machine translation. For this purpose, we provide training, lifelong and …
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SHAS: Approaching optimal Segmentation for End-to-End Speech Translation
2022 · arXiv (Cornell University)
Speech translation models are unable to directly process long audios, like TED talks, which have to be split into shorter segments. Speech translation datasets provide manual segmentations of the audios, which are not available in …
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Gender Bias in Multilingual Neural Machine Translation: The Architecture\n Matters
2020 · arXiv (Cornell University)
Multilingual Neural Machine Translation architectures mainly differ in the\namount of sharing modules and parameters among languages. In this paper, and\nfrom an algorithmic perspective, we explore if the chosen architecture, when\ntrained with the same data, influences …
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Efficient Speech Translation with Dynamic Latent Perceivers
2023
Transformers have been the dominant architecture for Speech Translation in recent years, achieving significant improvements in translation quality. Since speech signals are longer than their textual counterparts, and due to the quadratic complexity of the …
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A Primer on the Inner Workings of Transformer-based Language Models
2024 · arXiv (Cornell University)
The rapid progress of research aimed at interpreting the inner workings of advanced language models has highlighted a need for contextualizing the insights gained from years of work in this area. This primer provides a …
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Character-based neural machine translation
2016 · UPCommons institutional repository (Universitat Politècnica de Catalunya)
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 morphologically rich languages. In this paper, we propose a …
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Findings of the 2019 Conference on Machine Translation (WMT19)
2019
Loïc Barrault, Ondřej Bojar, Marta R. Costa-jussà, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Philipp Koehn, Shervin Malmasi, Christof Monz, Mathias Müller, Santanu Pal, Matt Post, Marcos Zampieri. Proceedings of the Fourth …
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No Language Left Behind: Scaling Human-Centered Machine Translation
2022 · arXiv (Cornell University)
Driven by the goal of eradicating language barriers on a global scale, machine translation has solidified itself as a key focus of artificial intelligence research today. However, such efforts have coalesced around a small subset …