Marcin Junczys-Dowmunt

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research

∙ 08/14/2023

SOTASTREAM: A Streaming Approach to Machine Translation Training

Many machine translation toolkits make use of a data preparation step wh...

0 Matt Post, et al. ∙

research

∙ 04/25/2023

Escaping the sentence-level paradigm in machine translation

It is well-known that document context is vital for resolving a range of...

0 Matt Post, et al. ∙

research

∙ 09/17/2021

The JHU-Microsoft Submission for WMT21 Quality Estimation Shared Task

This paper presents the JHU-Microsoft joint submission for WMT 2021 qual...

0 Shuoyang Ding, et al. ∙

research

∙ 09/12/2021

Levenshtein Training for Word-level Quality Estimation

We propose a novel scheme to use the Levenshtein Transformer to perform ...

0 Shuoyang Ding, et al. ∙

research

∙ 07/22/2021

To Ship or Not to Ship: An Extensive Evaluation of Automatic Metrics for Machine Translation

Automatic metrics are commonly used as the exclusive tool for declaring ...

0 Tom Kocmi, et al. ∙

research

∙ 04/21/2021

On User Interfaces for Large-Scale Document-Level Human Evaluation of Machine Translation Outputs

Recent studies emphasize the need of document context in human evaluatio...

0 Roman Grundkiewicz, et al. ∙

research

∙ 04/14/2021

The Curious Case of Hallucinations in Neural Machine Translation

In this work, we study hallucinations in Neural Machine Translation (NMT...

7 Vikas Raunak, et al. ∙

research

∙ 07/14/2019

Microsoft Translator at WMT 2019: Towards Large-Scale Document-Level Neural Machine Translation

This paper describes the Microsoft Translator submissions to the WMT19 n...

0 Marcin Junczys-Dowmunt, et al. ∙

research

∙ 09/01/2018

Dual Conditional Cross-Entropy Filtering of Noisy Parallel Corpora

In this work we introduce dual conditional cross-entropy filtering for n...

0 Marcin Junczys-Dowmunt, et al. ∙

research

∙ 09/01/2018

Microsoft's Submission to the WMT2018 News Translation Task: How I Learned to Stop Worrying and Love the Data

This paper describes the Microsoft submission to the WMT2018 news transl...

0 Marcin Junczys-Dowmunt, et al. ∙

research

∙ 09/01/2018

MS-UEdin Submission to the WMT2018 APE Shared Task: Dual-Source Transformer for Automatic Post-Editing

This paper describes the Microsoft and University of Edinburgh submissio...

0 Marcin Junczys-Dowmunt, et al. ∙

research

∙ 08/27/2018

Accelerating Asynchronous Stochastic Gradient Descent for Neural Machine Translation

In order to extract the best possible performance from asynchronous stoc...

0 Nikolay Bogoychev, et al. ∙

research

∙ 05/30/2018

Marian: Cost-effective High-Quality Neural Machine Translation in C++

This paper describes the submissions of the "Marian" team to the WNMT 20...

0 Marcin Junczys-Dowmunt, et al. ∙

research

∙ 04/16/2018

Near Human-Level Performance in Grammatical Error Correction with Hybrid Machine Translation

We combine two of the most popular approaches to automated Grammatical E...

0 Roman Grundkiewicz, et al. ∙

research

∙ 04/16/2018

Approaching Neural Grammatical Error Correction as a Low-Resource Machine Translation Task

Previously, neural methods in grammatical error correction (GEC) did not...

0 Marcin Junczys-Dowmunt, et al. ∙

research

∙ 04/01/2018

Marian: Fast Neural Machine Translation in C++

We present Marian, an efficient and self-contained Neural Machine Transl...

0 Marcin Junczys-Dowmunt, et al. ∙

research

∙ 03/15/2018

Achieving Human Parity on Automatic Chinese to English News Translation

Machine translation has made rapid advances in recent years. Millions of...

0 Hany Hassan, et al. ∙

research

∙ 06/13/2017

An Exploration of Neural Sequence-to-Sequence Architectures for Automatic Post-Editing

In this work, we explore multiple neural architectures adapted for the t...

0 Marcin Junczys-Dowmunt, et al. ∙

research

∙ 03/13/2017

Nematus: a Toolkit for Neural Machine Translation

We present Nematus, a toolkit for Neural Machine Translation. The toolki...

0 Rico Sennrich, et al. ∙

research

∙ 02/03/2017

Predicting Target Language CCG Supertags Improves Neural Machine Translation

Neural machine translation (NMT) models are able to partially learn synt...

0 Maria Nădejde, et al. ∙

research

∙ 10/13/2016

Fast, Scalable Phrase-Based SMT Decoding

The utilization of statistical machine translation (SMT) has grown enorm...

0 Hieu Hoang, et al. ∙

research

∙ 10/04/2016

Is Neural Machine Translation Ready for Deployment? A Case Study on 30 Translation Directions

In this paper we provide the largest published comparison of translation...

0 Marcin Junczys-Dowmunt, et al. ∙

research

∙ 07/05/2016

Target-Side Context for Discriminative Models in Statistical Machine Translation

Discriminative translation models utilizing source context have been sho...

0 Aleš Tamchyna, et al. ∙

research

∙ 05/20/2016

Phrase-based Machine Translation is State-of-the-Art for Automatic Grammatical Error Correction

In this work, we study parameter tuning towards the M^2 metric, the stan...

0 Marcin Junczys-Dowmunt, et al. ∙

research

∙ 05/16/2016

The AMU-UEDIN Submission to the WMT16 News Translation Task: Attention-based NMT Models as Feature Functions in Phrase-based SMT

This paper describes the AMU-UEDIN submissions to the WMT 2016 shared ta...

0 Marcin Junczys-Dowmunt, et al. ∙

research

∙ 05/16/2016

Log-linear Combinations of Monolingual and Bilingual Neural Machine Translation Models for Automatic Post-Editing

This paper describes the submission of the AMU (Adam Mickiewicz Universi...

0 Marcin Junczys-Dowmunt, et al. ∙

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