Search Results for author: Magdalena Biesialska

Found 8 papers, 2 papers with code

Findings of the 2021 Conference on Machine Translation (WMT21)

no code implementations WMT (EMNLP) 2021 Farhad Akhbardeh, Arkady Arkhangorodsky, Magdalena Biesialska, Ondřej Bojar, Rajen Chatterjee, Vishrav Chaudhary, Marta R. Costa-Jussa, Cristina España-Bonet, Angela Fan, Christian Federmann, Markus Freitag, Yvette Graham, Roman Grundkiewicz, Barry Haddow, Leonie Harter, Kenneth Heafield, Christopher Homan, Matthias Huck, Kwabena Amponsah-Kaakyire, Jungo Kasai, Daniel Khashabi, Kevin Knight, Tom Kocmi, Philipp Koehn, Nicholas Lourie, Christof Monz, Makoto Morishita, Masaaki Nagata, Ajay Nagesh, Toshiaki Nakazawa, Matteo Negri, Santanu Pal, Allahsera Auguste Tapo, Marco Turchi, Valentin Vydrin, Marcos Zampieri

This paper presents the results of the newstranslation task, the multilingual low-resourcetranslation for Indo-European languages, thetriangular translation task, and the automaticpost-editing task organised as part of the Con-ference on Machine Translation (WMT) 2021. In the news task, participants were asked tobuild machine translation systems for any of10 language pairs, to be evaluated on test setsconsisting mainly of news stories.

Machine Translation Translation

Continual Lifelong Learning in Natural Language Processing: A Survey

no code implementations COLING 2020 Magdalena Biesialska, Katarzyna Biesialska, Marta R. Costa-jussà

However, it is difficult for existing deep learning architectures to learn a new task without largely forgetting previously acquired knowledge.

Continual Learning

Sentiment Analysis with Contextual Embeddings and Self-Attention

no code implementations12 Mar 2020 Katarzyna Biesialska, Magdalena Biesialska, Henryk Rybinski

In natural language the intended meaning of a word or phrase is often implicit and depends on the context.

Sentiment Analysis

Refinement of Unsupervised Cross-Lingual Word Embeddings

1 code implementation21 Feb 2020 Magdalena Biesialska, Marta R. Costa-jussà

In this paper, we propose a self-supervised method to refine the alignment of unsupervised bilingual word embeddings.

Bilingual Lexicon Induction Cross-Lingual Word Embeddings +1

The TALP-UPC System for the WMT Similar Language Task: Statistical vs Neural Machine Translation

no code implementations WS 2019 Magdalena Biesialska, Lluis Guardia, Marta R. Costa-jussà

Although the problem of similar language translation has been an area of research interest for many years, yet it is still far from being solved.

Machine Translation Translation

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