IJSTR

International Journal of Scientific & Technology Research

Home About Us Scope Editorial Board Blog/Latest News Contact Us
0.2
2019CiteScore
 
10th percentile
Powered by  Scopus
Scopus coverage:
Nov 2018 to May 2020

CALL FOR PAPERS
AUTHORS
DOWNLOADS
CONTACT

IJSTR >> Volume 9 - Issue 9, September 2020 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Improving Quality Of Translation Using Ensemble Approach

[Full Text]

 

AUTHOR(S)

Deepti Chopra, Nisheeth Joshi

 

KEYWORDS

Machine Translation; Source Text Rewriting; Named Entity Translation

 

ABSTRACT

Quality improvement of Machine Translation (MT) is one of the open topics of research today. These days, there are many machine translators available online. But, these machine translators lack in the quality of translation when the input sentence is complex and consists of many Named Entities which are not identified correctly. In this paper, we address this problem by means of investigating a new ensemble approach that can help in improving the quality of translation from English to Hindi and Hindi to English. We have developed 6 MT systems that can perform English to Hindi Translation and 6 MT Systems that can perform Hindi to English Translation. We have developed Ensemble MT Systems by combining Statistical Machine Translation (SMT) with Source Text Rewriting and Named Entity Translation. We have compared translation using baseline approach (SMT) with the ensemble approach and have shown that quality of translation improves when we use ensemble approach for English-Hindi and Hindi-English Translation.

 

REFERENCES

[1]. Banerjee, S., &Lavie, A., “METEOR: An automatic metric for MT evaluation with improved correlation with human judgments”. In Proceedings of the acl workshop on intrinsic and extrinsic evaluation measures for machine translation and/or summarization,pp. 65-72, 2005..
[2]. Bott, S., &Saggion, H., “An unsupervised alignment algorithm for text simplification corpus construction”. In Proceedings of the Workshop on Monolingual Text-To-Text Generation . Association for Computational Linguistics, pp. 20-26, June 2011.
[3]. Ekbal, A., Naskar, S. K., & Bandyopadhyay, S,. “A modified joint source-channel model for transliteration”. In Proceedings of the COLING/ACL on Main conference poster sessions, Association for Computational Linguistics., pp. 191-198, July 2006.
[4]. Ekbal, A., Haque, R., Das, A., Poka, V., & Bandyopadhyay, S., “Language independent named entity recognition in Indian languages”. In Proceedings of the IJCNLP-08 Workshop on Named Entity Recognition for South and South East Asian Languages, 2008.
[5]. Jain, D., “Supervised Named Entity Recognition for Clinical Data”. In CLEF (Working Notes), 2015.
[6]. Joshi, N., Mathur, I., Darbari, H., & Kumar, A., “HEval: Yet another human evaluation metric”. arXiv preprint arXiv:1311.3961, 2013.
[7]. Joshi, N., Darbari, H., & Mathur, I., “HMM based POS tagger for Hindi”. In Proceeding of 2013 International Conference on Artificial Intelligence, Soft Computing (AISC-2013), 2013.
[8]. Kaur, Y., & Kaur, E., “Named Entity Recognition system for Hindi Language using combination of rule based approach and list look up approach”. International Journal of scientific research and management (IJSRM), 3(3), 2300-2306, 2015.
[9]. Papineni, K., Roukos, S., Ward, T., & Zhu, W. J.,“BLEU: a method for automatic evaluation of machine translation”. In Proceedings of the 40th annual meeting on association for computational linguistics,Association for Computational Linguistics, pp. 311-318, July 2002.
[10]. Poornima, C., Dhanalakshmi, V., Anand, K. M., &Soman, K. P., “Rule based sentence simplification for english to tamil machine translation system”. International Journal of Computer Applications, 25(8), pp. 38-42, 2011.
[11]. Soni, A., Jain, S., & Sharma, D. M., “Exploring verb frames for sentence simplification in hindi”. In Proceedings of the Sixth International oint Conference on Natural Language Processing, pp. 1082-1086, 2013.
[12]. Specia, L., Turchi, M., Cancedda, N., Dymetman, M., &Cristianini, N., “Estimating the sentence-level quality of machine translation systems”. In 13th Conference of the European Association for Machine Translation (pp. 28-37), May 2009
[13]. Srivastava, R., & Bhat, R. A.,“Transliteration systems across indian languages using parallel corpora”. In Proceedings of the 27th Pacific Asia Conference on Language, Information, and Computation (PACLIC 27) , pp. 390-398, 2013.
[14]. Vauquois, B., "A survey of formal grammars and algorithms for recognition and transformation in machine translation”, ifip congress-68, edinburgh, pp. 254-260; reprinted in ch. Bernard Vauquois et la TAO: ingt-cinq Ans de TraductionAutomatique-Analectes, pp. 201-213, 1968.
[15]. Vauquois, B., Veillon, G., &Veyrunes, J., “Syntax and Interpretation. Mechanical Translation and Computational Linguistics”. 9(2) ,pp. 44-54, 1966.