Volume 12, Issue 3 (12-2015)                   JSDP 2015, 12(3): 109-121 | Back to browse issues page

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Salehi M, Khadivi S, Riahi N. Confidence Estimation for Machine Translation using Novel Syntactic and Lexico-semantic Features. JSDP 2015; 12 (3) :109-121
URL: http://jsdp.rcisp.ac.ir/article-1-217-en.html
alzahra university
Abstract:   (5165 Views)

Despite machine translation (MT) wide suc-cess over last years, this technology is still not able to exactly translate text so that except for some language pairs in certain domains, post editing its output may take longer time than human translation. Nevertheless by having an estimation of the output quality, users can manage imperfection of this tech-nology. It means we need to estimate the confidence of the output without having any references. Moreover, Confidence Estimation (CE) can be useful for some applications that their goal is to improve machine translation quality such as system combination, regener-ating, pruning, etc. but there is not yet any completely satisfactory method for CE task. We propose 5 groups of syntactic and lexico-semantic features. The results show that the lexico-semantic feature outperforms the best baseline system (2) by 9.63% in CER, 8.5% in F-measure and 5.1% in negative class F-measure. Also by combining proposed syn-tactic features together we reach 4.59% CER reduction, 4.1% F-measure improvement and 2% negative F-measure improvement.

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Type of Study: Applicable | Subject: Paper
Received: 2014/02/23 | Accepted: 2015/09/8 | Published: 2016/01/4 | ePublished: 2016/01/4

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