Evaluation of Domain Adaptation Approaches to Improve the Translation Quality

Author(s):  
Ezgi Yıldırım ◽  
Ahmet Cüneyd Tantuğ
2017 ◽  
Vol 108 (1) ◽  
pp. 283-294 ◽  
Author(s):  
Álvaro Peris ◽  
Mara Chinea-Ríos ◽  
Francisco Casacuberta

AbstractCorpora are precious resources, as they allow for a proper estimation of statistical machine translation models. Data selection is a variant of the domain adaptation field, aimed to extract those sentences from an out-of-domain corpus that are the most useful to translate a different target domain. We address the data selection problem in statistical machine translation as a classification task. We present a new method, based on neural networks, able to deal with monolingual and bilingual corpora. Empirical results show that our data selection method provides slightly better translation quality, compared to a state-of-the-art method (cross-entropy), requiring substantially less data. Moreover, the results obtained are coherent across different language pairs, demonstrating the robustness of our proposal.


2015 ◽  
Author(s):  
Raghuraman Gopalan ◽  
Ruonan Li ◽  
Vishal M. Patel ◽  
Rama Chellappa

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Ryuki Tachibana ◽  
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Author(s):  
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Heinrich Dinkel ◽  
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Keyword(s):  

2020 ◽  
Vol 155 ◽  
pp. 113404 ◽  
Author(s):  
Peng Liu ◽  
Ting Xiao ◽  
Cangning Fan ◽  
Wei Zhao ◽  
Xianglong Tang ◽  
...  

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