Marcin Junczys-Dowmunt
6 papers in the PaperMetrix corpus
Papers by this author
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Is Neural Machine Translation Ready for Deployment? A Case Study on 30 Translation Directions
2016 · arXiv (Cornell University)
In this paper we provide the largest published comparison of translation quality for phrase-based SMT and neural machine translation across 30 translation directions. For ten directions we also include hierarchical phrase-based MT. Experiments are performed …
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The United Nations Parallel Corpus v1.0
2016
This paper describes the creation process and statistics of the official United Nations Parallel Corpus, the first parallel corpus composed from United Nations documents published by the original data creator.The parallel corpus presented consists of …
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Nematus: a Toolkit for Neural Machine Translation
2017
Rico Sennrich, Orhan Firat, Kyunghyun Cho, Alexandra Birch, Barry Haddow, Julian Hitschler, Marcin Junczys-Dowmunt, Samuel Läubli, Antonio Valerio Miceli Barone, Jozef Mokry, Maria Nădejde. Proceedings of the Software Demonstrations of the 15th Conference of the …
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Predicting Target Language CCG Supertags Improves Neural Machine Translation
2017
Neural machine translation (NMT) models are able to partially learn syntactic information from sequential lexical information. Still, some complex syntactic phenomena such as prepositional phrase attachment are poorly modeled. This work aims to answer two …
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Achieving Human Parity on Automatic Chinese to English News Translation
2018 · arXiv (Cornell University)
Machine translation has made rapid advances in recent years. Millions of people are using it today in online translation systems and mobile applications in order to communicate across language barriers. The question naturally arises whether …
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Neural Grammatical Error Correction Systems with Unsupervised Pre-training on Synthetic Data
2019
Considerable effort has been made to address the data sparsity problem in neural grammatical error correction. In this work, we propose a simple and surprisingly effective unsupervised synthetic error generation method based on confusion sets …