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Multi-Engine Machine Translation of Technical E-Contents from English to Hindi: Evaluated by Fluency & Adequacy

Published: 06 May 2016 Publication History

Abstract

Machine translation engines are helpful to convert translation from one source language to other target language with ease of mind for the native user. The status of machine translation engines is good when it is used only for indicative reference on the resultant translation and least dependency on it. The native user who want to use the machine translation system and highly dependent on it then the quality of translation needs to be well refined.
The empirical study proves that the existing machine translation engines for English to Hindi language pair are not exhibiting the fluent and adequate output translation on which the native user may dependent on it. The rigorous testing performed on computer science domain related e-contents on different freely available engines. This study has concluded with certain remarks which will be accounted for development of best architecture of machine translation engines for technical domain e-contents especially for computer science.

References

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Anuvadaksh, 2015. A TDIL, Indian Translation Project, Used as component engine for testing purpose, May 2015, from: http://tdildc.in/components/com_mtsystem/CommonUI/homeMT.php
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Brihaspati, (The Virtual Classroom website), 2015. Retrieved e-contents on May, 2015, from: http://brihaspati.nmeict.in/brihaspati/servlet/brihaspati/template/call,ViewFileContent.vm/topic/lecturenotes/type/content/filename/Testbed-ip.pdf
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Chris Callison-Burch and Raymond S. Flournoy. 2001. A Program for Automatically Selecting the Best Output from Multiple Machine Translation Engines. In Proceedings of the Machine Translation Summit VIII.
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E. Hovy, & Margaret King. 2002. Machine Translation. In Kluwer Academic Publishers. Printed in the Netherlands, 43--75.
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Google Translate Engine, 2015. Used as component engine for testing purpose, May, 2015, form http://translate.google.com/
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Goswami, P.K. and Dwivedi, S.K. 2014. An empirical study on English to Hindi E-contents Machine Translation through multi engines. In proceedings of 3rd International Conference on Reliability, Infocom Technologies and Optimization (ICRITO-2014, Trends and Future Directions, ISBN: 978-93-83083-99-2), 536--542.
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Graeme Blackwood, Adrià de Gispert, and William Byrne. 2010. Fluency Constraints for Minimum Bayes-Risk Decoding of Statistical Machine Translation Lattices. In proceedings of the 23rd International Conference on Computational Linguistics, (Coling-2010, Beijing), 71--79.
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Khan Academy website, 2015. Retrieved e-contents on May, 2015, from https://www.khanacademy.org/
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Microsoft Bing Translation Engine (Bing, Beta version), 2015. Used as component engine for testing purpose, May, 2015, from http://www.bing.com/translator/
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Moses, 2013. A Statistical Machine Translation System (Moses). Retrieved e-contents on May, 2015, from http://www.statmt.org/moses/
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Xiaoyi Ma & Christopher Cieri. 2006. Corpus Support for Machine Translation at LDC. Philadelphia.
  1. Multi-Engine Machine Translation of Technical E-Contents from English to Hindi: Evaluated by Fluency & Adequacy

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    cover image ACM Other conferences
    WCCCE '16: Proceedings of the 21st Western Canadian Conference on Computing Education
    May 2016
    137 pages
    ISBN:9781450343558
    DOI:10.1145/2910925
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 06 May 2016

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    Author Tags

    1. Example Based Machine Translation (EBMT)
    2. Fluency and Adequacy (F&A)
    3. Machine Translation (MT)
    4. Multi Engine Machine Translation (MEMT)
    5. Multi Engine Machine Translation for English to Hindi Language (MEMTEHiL)
    6. Rule Based Machine Translation (RBMT)
    7. Statistical Machine Translation (SMT)

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    WCCCE '16

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    WCCCE '16 Paper Acceptance Rate 26 of 35 submissions, 74%;
    Overall Acceptance Rate 78 of 117 submissions, 67%

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