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A Systematic Comparison of Data Selection Criteria for SMT Domain Adaptation
Data selection has shown significant improvements in effective use of training data by extracting sentences from large general-domain corpora to adapt statistical machine translation (SMT) systems to in-domain data. This paper performs an in-depth analysis of three different sentence selection techn...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3934767/ https://www.ncbi.nlm.nih.gov/pubmed/24683356 http://dx.doi.org/10.1155/2014/745485 |
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author | Wang, Longyue Wong, Derek F. Chao, Lidia S. Lu, Yi Xing, Junwen |
author_facet | Wang, Longyue Wong, Derek F. Chao, Lidia S. Lu, Yi Xing, Junwen |
author_sort | Wang, Longyue |
collection | PubMed |
description | Data selection has shown significant improvements in effective use of training data by extracting sentences from large general-domain corpora to adapt statistical machine translation (SMT) systems to in-domain data. This paper performs an in-depth analysis of three different sentence selection techniques. The first one is cosine tf-idf, which comes from the realm of information retrieval (IR). The second is perplexity-based approach, which can be found in the field of language modeling. These two data selection techniques applied to SMT have been already presented in the literature. However, edit distance for this task is proposed in this paper for the first time. After investigating the individual model, a combination of all three techniques is proposed at both corpus level and model level. Comparative experiments are conducted on Hong Kong law Chinese-English corpus and the results indicate the following: (i) the constraint degree of similarity measuring is not monotonically related to domain-specific translation quality; (ii) the individual selection models fail to perform effectively and robustly; but (iii) bilingual resources and combination methods are helpful to balance out-of-vocabulary (OOV) and irrelevant data; (iv) finally, our method achieves the goal to consistently boost the overall translation performance that can ensure optimal quality of a real-life SMT system. |
format | Online Article Text |
id | pubmed-3934767 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-39347672014-03-30 A Systematic Comparison of Data Selection Criteria for SMT Domain Adaptation Wang, Longyue Wong, Derek F. Chao, Lidia S. Lu, Yi Xing, Junwen ScientificWorldJournal Research Article Data selection has shown significant improvements in effective use of training data by extracting sentences from large general-domain corpora to adapt statistical machine translation (SMT) systems to in-domain data. This paper performs an in-depth analysis of three different sentence selection techniques. The first one is cosine tf-idf, which comes from the realm of information retrieval (IR). The second is perplexity-based approach, which can be found in the field of language modeling. These two data selection techniques applied to SMT have been already presented in the literature. However, edit distance for this task is proposed in this paper for the first time. After investigating the individual model, a combination of all three techniques is proposed at both corpus level and model level. Comparative experiments are conducted on Hong Kong law Chinese-English corpus and the results indicate the following: (i) the constraint degree of similarity measuring is not monotonically related to domain-specific translation quality; (ii) the individual selection models fail to perform effectively and robustly; but (iii) bilingual resources and combination methods are helpful to balance out-of-vocabulary (OOV) and irrelevant data; (iv) finally, our method achieves the goal to consistently boost the overall translation performance that can ensure optimal quality of a real-life SMT system. Hindawi Publishing Corporation 2014-02-11 /pmc/articles/PMC3934767/ /pubmed/24683356 http://dx.doi.org/10.1155/2014/745485 Text en Copyright © 2014 Longyue Wang et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Wang, Longyue Wong, Derek F. Chao, Lidia S. Lu, Yi Xing, Junwen A Systematic Comparison of Data Selection Criteria for SMT Domain Adaptation |
title | A Systematic Comparison of Data Selection Criteria for SMT Domain Adaptation |
title_full | A Systematic Comparison of Data Selection Criteria for SMT Domain Adaptation |
title_fullStr | A Systematic Comparison of Data Selection Criteria for SMT Domain Adaptation |
title_full_unstemmed | A Systematic Comparison of Data Selection Criteria for SMT Domain Adaptation |
title_short | A Systematic Comparison of Data Selection Criteria for SMT Domain Adaptation |
title_sort | systematic comparison of data selection criteria for smt domain adaptation |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3934767/ https://www.ncbi.nlm.nih.gov/pubmed/24683356 http://dx.doi.org/10.1155/2014/745485 |
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