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Assessment of Multi-Engine Machine Translation for English to Hindi Language (MEMTEHiL): Using F&A and iBLEU Metrics

Assessment of Multi-Engine Machine Translation for English to Hindi Language (MEMTEHiL): Using F&A and iBLEU Metrics

Pankaj K. Goswami (Amity University, Noida, India), Sanjay K. Dwivedi (Babasaheb Bhimrao Ambedkar University, Lucknow, India), and C. K. Jha (Banasthali University, Rajasthan, India)
Copyright: © 2016 |Pages: 16
DOI: 10.4018/IJALR.2016010102
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Abstract

English to Hindi translation of the computer-science related e-content, generated through an online freely available machine translation engine may not be technically correct. The expected target translation should be as fluent as intended for the native learners and the meaning of a source e-content should be conveyed properly. A Multi-Engine Machine Translation for English to Hindi Language (MEMTEHiL) framework has been designed and integrated by the authors as a translation solution for the computer science domain e-content. It was possible by enabling the use of well-tested approaches of machine translation. The humanly evaluated and acceptable metrics like fluency and adequacy (F&A) were used to assess the best translation quality for English to Hindi language pair. Besides humanly-judged metrics, another well-tested and existing interactive version of Bi-Lingual Evaluation Understudy (iBLEU) was used for evaluation. Authors have incorporated both parameters (F&A and iBLEU) for assessing the quality of translation as regenerated by the designed MEMTEHiL.
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Challenges For Mt Evaluation

Ideally, the quality of machine translated e-content, assessed by the reference text. Reference text supposed to be the perfect translation of the source e-content. Based on the reference text, the quality of the machine translated e-content can be evaluated. The deviation and matching from the reference text are actually deciding the translation quality. This concept of reference matching with the translated content has been already successfully experimented by various researchers for the evaluation of machine translation through metrics. (Kalyani, 2014).

An e-content translation if perfectly done by a skilled human expert who is conversant with both source and target languages. Then this translation is referred as human-reference translation. It is actually the perfect translation of the source e-content, and that is expected quality by the machine translation.

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