Data selection and smoothing in an open-source system for the 2008 NIST machine translation evaluation

Author(s):  
Holger Schwenk ◽  
Yannick Esteve
2015 ◽  
Vol 104 (1) ◽  
pp. 17-26
Author(s):  
Miloš Stanojević ◽  
Khalil Sima’an

Abstract We present BEER, an open source implementation of a machine translation evaluation metric. BEER is a metric trained for high correlation with human ranking by using learning-to-rank training methods. For evaluation of lexical accuracy it uses sub-word units (character n-grams) while for measuring word order it uses hierarchical representations based on PETs (permutation trees). During the last WMT metrics tasks, BEER has shown high correlation with human judgments both on the sentence and the corpus levels. In this paper we will show how BEER can be used for (i) full evaluation of MT output, (ii) isolated evaluation of word order and (iii) tuning MT systems.


2014 ◽  
Vol 102 (1) ◽  
pp. 69-80 ◽  
Author(s):  
Torregrosa Daniel ◽  
Forcada Mikel L. ◽  
Pérez-Ortiz Juan Antonio

Abstract We present a web-based open-source tool for interactive translation prediction (ITP) and describe its underlying architecture. ITP systems assist human translators by making context-based computer-generated suggestions as they type. Most of the ITP systems in literature are strongly coupled with a statistical machine translation system that is conveniently adapted to provide the suggestions. Our system, however, follows a resource-agnostic approach and suggestions are obtained from any unmodified black-box bilingual resource. This paper reviews our ITP method and describes the architecture of Forecat, a web tool, partly based on the recent technology of web components, that eases the use of our ITP approach in any web application requiring this kind of translation assistance. We also evaluate the performance of our method when using an unmodified Moses-based statistical machine translation system as the bilingual resource.


Author(s):  
Guillaume Klein ◽  
Yoon Kim ◽  
Yuntian Deng ◽  
Jean Senellart ◽  
Alexander Rush

2021 ◽  
Author(s):  
Vânia Mendonça ◽  
Ricardo Rei ◽  
Luisa Coheur ◽  
Alberto Sardinha ◽  
Ana Lúcia Santos

Author(s):  
Yu Chen ◽  
Andreas Eisele ◽  
Christian Federmann ◽  
Eva Hasler ◽  
Michael Jellinghaus ◽  
...  

2020 ◽  
Author(s):  
Wei Zhao ◽  
Goran Glavaš ◽  
Maxime Peyrard ◽  
Yang Gao ◽  
Robert West ◽  
...  

Author(s):  
Tanmai Khanna ◽  
Jonathan N. Washington ◽  
Francis M. Tyers ◽  
Sevilay Bayatlı ◽  
Daniel G. Swanson ◽  
...  

AbstractThis paper presents an overview of Apertium, a free and open-source rule-based machine translation platform. Translation in Apertium happens through a pipeline of modular tools, and the platform continues to be improved as more language pairs are added. Several advances have been implemented since the last publication, including some new optional modules: a module that allows rules to process recursive structures at the structural transfer stage, a module that deals with contiguous and discontiguous multi-word expressions, and a module that resolves anaphora to aid translation. Also highlighted is the hybridisation of Apertium through statistical modules that augment the pipeline, and statistical methods that augment existing modules. This includes morphological disambiguation, weighted structural transfer, and lexical selection modules that learn from limited data. The paper also discusses how a platform like Apertium can be a critical part of access to language technology for so-called low-resource languages, which might be ignored or deemed unapproachable by popular corpus-based translation technologies. Finally, the paper presents some of the released and unreleased language pairs, concluding with a brief look at some supplementary Apertium tools that prove valuable to users as well as language developers. All Apertium-related code, including language data, is free/open-source and available at https://github.com/apertium.


Author(s):  
Taisiya Glushkova ◽  
Chrysoula Zerva ◽  
Ricardo Rei ◽  
André F. T. Martins

Sign in / Sign up

Export Citation Format

Share Document