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Predicting Human Assessment of Machine Translation Quality by Combining Automatic Evaluation Metrics using Binary Classifiers

by Michael Paul, andrew Finch, Eiichiro Sumita
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 59 - Number 10
Year of Publication: 2012
Authors: Michael Paul, andrew Finch, Eiichiro Sumita
10.5120/9581-4062

Michael Paul, andrew Finch, Eiichiro Sumita . Predicting Human Assessment of Machine Translation Quality by Combining Automatic Evaluation Metrics using Binary Classifiers. International Journal of Computer Applications. 59, 10 ( December 2012), 1-7. DOI=10.5120/9581-4062

@article{ 10.5120/9581-4062,
author = { Michael Paul, andrew Finch, Eiichiro Sumita },
title = { Predicting Human Assessment of Machine Translation Quality by Combining Automatic Evaluation Metrics using Binary Classifiers },
journal = { International Journal of Computer Applications },
issue_date = { December 2012 },
volume = { 59 },
number = { 10 },
month = { December },
year = { 2012 },
issn = { 0975-8887 },
pages = { 1-7 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume59/number10/9581-4062/ },
doi = { 10.5120/9581-4062 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:03:47.573075+05:30
%A Michael Paul
%A andrew Finch
%A Eiichiro Sumita
%T Predicting Human Assessment of Machine Translation Quality by Combining Automatic Evaluation Metrics using Binary Classifiers
%J International Journal of Computer Applications
%@ 0975-8887
%V 59
%N 10
%P 1-7
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents a method to predict human assessments of machine translation (MT) quality based on a combination of binary classifiers using a coding matrix. The multiclass categorization problem is reduced to a set of binary problems that are solved using standard classification learning algorithms trained on the results of multiple automatic evaluation metrics. Experimental results using a large-scale human-annotated evaluation corpus show that the decomposition into binary classifiers achieves higher classification accuracies than the multiclass categorization problem. In addition, the proposed method achieves a higher correlation with human judgments on the sentence level compared to standard automatic evaluation measures.

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Index Terms

Computer Science
Information Sciences

Keywords

Evaluation Metric Combination Human Assessment Predictionifx

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