Michael Denkowski


2020

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Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)
Michael Denkowski | Christian Federmann
Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)

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The Sockeye 2 Neural Machine Translation Toolkit at AMTA 2020
Tobias Domhan | Michael Denkowski | David Vilar | Xing Niu | Felix Hieber | Kenneth Heafield
Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)

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Sockeye 2: A Toolkit for Neural Machine Translation
Felix Hieber | Tobias Domhan | Michael Denkowski | David Vilar
Proceedings of the 22nd Annual Conference of the European Association for Machine Translation

We present Sockeye 2, a modernized and streamlined version of the Sockeye neural machine translation (NMT) toolkit. New features include a simplified code base through the use of MXNet’s Gluon API, a focus on state of the art model architectures, and distributed mixed precision training. These improvements result in faster training and inference, higher automatic metric scores, and a shorter path from research to production.

2018

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The Sockeye Neural Machine Translation Toolkit at AMTA 2018
Felix Hieber | Tobias Domhan | Michael Denkowski | David Vilar | Artem Sokolov | Ann Clifton | Matt Post
Proceedings of the 13th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)

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Bi-Directional Neural Machine Translation with Synthetic Parallel Data
Xing Niu | Michael Denkowski | Marine Carpuat
Proceedings of the 2nd Workshop on Neural Machine Translation and Generation

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. We propose a novel technique that combines back-translation and multilingual NMT to improve performance in these difficult cases. Our technique trains a single model for both directions of a language pair, allowing us to back-translate source or target monolingual data without requiring an auxiliary model. We then continue training on the augmented parallel data, enabling a cycle of improvement for a single model that can incorporate any source, target, or parallel data to improve both translation directions. As a byproduct, these models can reduce training and deployment costs significantly compared to uni-directional models. Extensive experiments show that our technique outperforms standard back-translation in low-resource scenarios, improves quality on cross-domain tasks, and effectively reduces costs across the board.

2017

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Stronger Baselines for Trustable Results in Neural Machine Translation
Michael Denkowski | Graham Neubig
Proceedings of the First Workshop on Neural Machine Translation

Interest in neural machine translation has grown rapidly as its effectiveness has been demonstrated across language and data scenarios. New research regularly introduces architectural and algorithmic improvements that lead to significant gains over “vanilla” NMT implementations. However, these new techniques are rarely evaluated in the context of previously published techniques, specifically those that are widely used in state-of-the-art production and shared-task systems. As a result, it is often difficult to determine whether improvements from research will carry over to systems deployed for real-world use. In this work, we recommend three specific methods that are relatively easy to implement and result in much stronger experimental systems. Beyond reporting significantly higher BLEU scores, we conduct an in-depth analysis of where improvements originate and what inherent weaknesses of basic NMT models are being addressed. We then compare the relative gains afforded by several other techniques proposed in the literature when starting with vanilla systems versus our stronger baselines, showing that experimental conclusions may change depending on the baseline chosen. This indicates that choosing a strong baseline is crucial for reporting reliable experimental results.

2014

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Learning from Post-Editing: Online Model Adaptation for Statistical Machine Translation
Michael Denkowski | Chris Dyer | Alon Lavie
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics

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Real Time Adaptive Machine Translation for Post-Editing with cdec and TransCenter
Michael Denkowski | Alon Lavie | Isabel Lacruz | Chris Dyer
Proceedings of the EACL 2014 Workshop on Humans and Computer-assisted Translation

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Meteor Universal: Language Specific Translation Evaluation for Any Target Language
Michael Denkowski | Alon Lavie
Proceedings of the Ninth Workshop on Statistical Machine Translation

2013

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The CMU Machine Translation Systems at WMT 2013: Syntax, Synthetic Translation Options, and Pseudo-References
Waleed Ammar | Victor Chahuneau | Michael Denkowski | Greg Hanneman | Wang Ling | Austin Matthews | Kenton Murray | Nicola Segall | Alon Lavie | Chris Dyer
Proceedings of the Eighth Workshop on Statistical Machine Translation

2012

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The CMU-Avenue French-English Translation System
Michael Denkowski | Greg Hanneman | Alon Lavie
Proceedings of the Seventh Workshop on Statistical Machine Translation

2011

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Meteor 1.3: Automatic Metric for Reliable Optimization and Evaluation of Machine Translation Systems
Michael Denkowski | Alon Lavie
Proceedings of the Sixth Workshop on Statistical Machine Translation

2010

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Extending the METEOR Machine Translation Evaluation Metric to the Phrase Level
Michael Denkowski | Alon Lavie
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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Exploring Normalization Techniques for Human Judgments of Machine Translation Adequacy Collected Using Amazon Mechanical Turk
Michael Denkowski | Alon Lavie
Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon’s Mechanical Turk

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Turker-Assisted Paraphrasing for English-Arabic Machine Translation
Michael Denkowski | Hassan Al-Haj | Alon Lavie
Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon’s Mechanical Turk

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METEOR-NEXT and the METEOR Paraphrase Tables: Improved Evaluation Support for Five Target Languages
Michael Denkowski | Alon Lavie
Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR