Martin Popel
6 papers in the PaperMetrix corpus
Papers by this author
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Findings of the 2016 Conference on Machine Translation
2016
Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Varvara Logacheva, Christof Monz, Matteo Negri, Aurélie Névéol, Mariana Neves, Martin Popel, Matt Post, Raphael Rubino, Carolina Scarton, …
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CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
2017
Daniel Zeman, Martin Popel, Milan Straka, Jan Hajič, Joakim Nivre, Filip Ginter, Juhani Luotolahti, Sampo Pyysalo, Slav Petrov, Martin Potthast, Francis Tyers, Elena Badmaeva, Memduh Gokirmak, Anna Nedoluzhko, Silvie Cinková, Jan Hajič jr., Jaroslava Hlaváčová, …
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Training Tips for the Transformer Model
2018 · The Prague Bulletin of Mathematical Linguistics
Abstract This article describes our experiments in neural machine translation using the recent Tensor2Tensor framework and the Transformer sequence-to-sequence model (Vaswani et al., 2017). We examine some of the critical parameters that affect the final …
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CUNI Transformer Neural MT System for WMT18
2018
We describe our NMT system submitted to the WMT2018 shared task in news translation. Our system is based on the Transformer model We use an improved technique of backtranslation, where we iterate the process of …
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CoNLL 2018 Shared Task : Multilingual Parsing from Raw Text to Universal Dependencies
2018 · Conference on Computational Natural Language Learning
Every year, the Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their learning systems on the same data sets. In 2018, one of two tasks was …
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Transforming machine translation: a deep learning system reaches news translation quality comparable to human professionals
2020 · Nature Communications
The quality of human translation was long thought to be unattainable for computer translation systems. In this study, we present a deep-learning system, CUBBITT, which challenges this view. In a context-aware blind evaluation by human …