scholarly journals Z-MERT: A Fully Configurable Open Source Tool for Minimum Error Rate Training of Machine Translation Systems

2009 ◽  
Vol 91 (1) ◽  
pp. 79-88 ◽  
Author(s):  
Omar Zaidan
Keyword(s):  
Open Source ◽  
Machine Translation ◽  
Error Rate ◽  
Software Tool ◽  
Minimum Error ◽  
Open Source Tool ◽  
Series Of Experiments ◽  
Evaluation Metric ◽  
Minimum Error Rate Training ◽  
Translation Systems

Z-MERT: A Fully Configurable Open Source Tool for Minimum Error Rate Training of Machine Translation Systems We introduce Z-MERT, a software tool for minimum error rate training of machine translation systems (Och, 2003). In addition to being an open source tool that is extremely easy to compile and run, Z-MERT is also agnostic regarding the evaluation metric, fully configurable, and requires no modification to work with any decoder. We describe Z-MERT and review its features, and report the results of a series of experiments that examine the tool's runtime. We establish that Z-MERT is extremely efficient, making it well-suited for time-sensitive pipelines. The experiments also provide an insight into the tool's runtime in terms of several variables (size of the development set, size of produced N-best lists, etc).

Margin Infused Relaxed Algorithm for Moses

2011 ◽  
Vol 96 (1) ◽  
pp. 69-78 ◽  
Author(s):  
Eva Hasler ◽  
Barry Haddow ◽  
Philipp Koehn
Keyword(s):  
Open Source ◽  
Machine Translation ◽  
Error Rate ◽  
Statistical Machine Translation ◽  
Experimental Results ◽  
Minimum Error ◽  
Feature Sets ◽  
Translation Quality ◽  
Core Feature ◽  
Minimum Error Rate Training

Margin Infused Relaxed Algorithm for Moses We describe an open-source implementation of the Margin Infused Relaxed Algorithm (MIRA) for statistical machine translation (SMT). The implementation is part of the Moses toolkit and can be used as an alternative to standard minimum error rate training (MERT). A description of the implementation and its usage on core feature sets as well as large, sparse feature sets is given and we report experimental results comparing the performance of MIRA with MERT in terms of translation quality and stability.

scholarly journals Improved Minimum Error Rate Training in Moses

2009 ◽  
Vol 91 (1) ◽  
Author(s):  
Nicola Bertoldi ◽  
Barry Haddow ◽  
Jean-Baptiste Fouet
Keyword(s):  
Error Rate ◽  
Minimum Error ◽  
Minimum Error Rate Training

scholarly journals Integration of a Multilingual Preordering Component into a Commercial SMT Platform

2017 ◽  
Vol 108 (1) ◽  
pp. 61-72
Author(s):  
Anita Ramm ◽  
Riccardo Superbo ◽  
Dimitar Shterionov ◽  
Tony O’Dowd ◽  
Alexander Fraser
Keyword(s):  
Open Source ◽  
Machine Translation ◽  
Long Range ◽  
Significant Role ◽  
Processing Speed ◽  
Statistical Machine Translation ◽  
Neural Machine Translation ◽  
Open Source Tool

AbstractWe present a multilingual preordering component tailored for a commercial Statistical Machine translation platform. In commercial settings, issues such as processing speed as well as the ability to adapt models to the customers’ needs play a significant role and have a big impact on the choice of approaches that are added to the custom pipeline to deal with specific problems such as long-range reorderings.We developed a fast and customisable preordering component, also available as an open-source tool, which comes along with a generic implementation that is restricted neither to the translation platform nor to the Machine Translation paradigm. We test preordering on three language pairs: English →Japanese/German/Chinese for both Statistical Machine Translation (SMT) and Neural Machine Translation (NMT). Our experiments confirm previously reported improvements in the SMT output when the models are trained on preordered data, but they also show that preordering does not improve NMT.

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