The QT21/HimL Combined Machine Translation System

Peter, J. Dinesh; Alkhouli, Tamer; Ney, Hermann; Huck, Matthias; Braune, Fabienne; Fraser, Alexander; Tamchyna, Aleš; Bojar, Ondřej; Haddow, Barry; Sennrich, Rico; Blain, Frédéric; Specia, Lucia; Niehues, Jan; Waibel, Alex; Allauzen, Alexandre; Aufrant, Lauriane; Burlot, Franck; Knyazeva, Elena; Lee, Thomas; Yvon, François; Pinnis, Mārcis; Frank, Stella

doi:10.18653/v1/w16-2320

Cited by 10 publications

(7 citation statements)

References 41 publications

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“…Similar to English→German, we apply our APE model on the top 2 submissions of the WMT16 evaluation campaign (Table 6). Both the QT21 submission (Peter et al, 2016), which is a system combination of several NMT systems,…”

Section: English→romanianmentioning

confidence: 99%

APE at Scale and Its Implications on MT Evaluation Biases

Freitag¹,

Caswell²,

Roy³

2019

Proceedings of the Fourth Conference on Machine Translation (Volume 1: Research Papers)

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In this work, we train an Automatic Post-Editing (APE) model and use it to reveal biases in standard Machine Translation (MT) evaluation procedures. The goal of our APE model is to correct typical errors introduced by the translation process, and convert the "translationese" output into natural text. Our APE model is trained entirely on monolingual data that has been round-trip translated through English, to mimic errors that are similar to the ones introduced by NMT. We apply our model to the output of existing NMT systems, and demonstrate that, while the human-judged quality improves in all cases, BLEU scores drop with forward-translated test sets. We verify these results for the WMT18 English→German, WMT15 English→French, and WMT16 English→Romanian tasks. Furthermore, we selectively apply our APE model on the output of the top submissions of the most recent WMT evaluation campaigns. We see quality improvements on all tasks of up to 2.5 BLEU points.

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Section: English→romanianmentioning

confidence: 99%

APE at Scale and Its Implications on MT Evaluation Biases

Freitag¹,

Caswell²,

Roy³

2019

Proceedings of the Fourth Conference on Machine Translation (Volume 1: Research Papers)

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“…Specifically, we participated in the unsupervised learning task which focuses on training MT models without access to any parallel data. The team has a strong track record at previous WMT shared tasks (Bojar et al, 2017(Bojar et al, , 2015(Bojar et al, , 2014(Bojar et al, , 2013 working on SMT systems (Cap et al, 2014(Cap et al, , 2015Weller et al, 2013;Peter et al, 2016; and proposed a top scoring linguistically informed neural machine translation system based on human evaluation at WMT17.…”

Section: Introductionmentioning

confidence: 99%

The LMU Munich Unsupervised Machine Translation Systems

Stojanovski¹,

Hangya²,

Huck³

et al. 2018

Proceedings of the Third Conference on Machine Translation: Shared Task Papers

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We describe LMU Munich's unsupervised machine translation systems for English↔German translation. These systems were used to participate in the WMT18 news translation shared task and more specifically, for the unsupervised learning sub-track. The systems are trained on English and German monolingual data only and exploit and combine previously proposed techniques such as using word-byword translated data based on bilingual word embeddings, denoising and on-the-fly backtranslation.

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“…Research on various different types of machine translation models has previously been conducted at LMU. Core SMT paradigms for LMU's past shared task participations include phrase-based models (Cap et al, 2015(Cap et al, , 2014bWeller et al, 2013;, hierarchical phrasebased models Peter et al, 2016), operation sequence models , and hybrids of statistical approaches with rule-based and deep syntactic components (Tamchyna et al, 2016b).…”

Section: Introductionmentioning

confidence: 99%