Search Results for author: Michael Denkowski

Found 10 papers, 4 papers with code

The Sockeye 2 Neural Machine Translation Toolkit at AMTA 2020

1 code implementation11 Aug 2020 Tobias Domhan, Michael Denkowski, David Vilar, Xing Niu, Felix Hieber, Kenneth Heafield

We present Sockeye 2, a modernized and streamlined version of the Sockeye neural machine translation (NMT) toolkit.

Machine Translation Quantization +1

Bi-Directional Neural Machine Translation with Synthetic Parallel Data

no code implementations WS 2018 Xing Niu, Michael Denkowski, Marine Carpuat

Despite impressive progress in high-resource settings, Neural Machine Translation (NMT) still struggles in low-resource and out-of-domain scenarios, often failing to match the quality of phrase-based translation.

Machine Translation Translation

Sockeye: A Toolkit for Neural Machine Translation

14 code implementations15 Dec 2017 Felix Hieber, Tobias Domhan, Michael Denkowski, David Vilar, Artem Sokolov, Ann Clifton, Matt Post

Written in Python and built on MXNet, the toolkit offers scalable training and inference for the three most prominent encoder-decoder architectures: attentional recurrent neural networks, self-attentional transformers, and fully convolutional networks.

Machine Translation Translation

Stronger Baselines for Trustable Results in Neural Machine Translation

1 code implementation WS 2017 Michael Denkowski, Graham Neubig

As a result, it is often difficult to determine whether improvements from research will carry over to systems deployed for real-world use.

Machine Translation Translation

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