Corpus Based Machine Translation for Scientific Text - Artificial Intelligence Applications and Innovations (AIAI 2018) Access content directly
Conference Papers Year : 2018

Corpus Based Machine Translation for Scientific Text

Abstract

From many years, machine translation and computational linguistic research community has given immense attention towards the development of machine translation techniques. In order to fulfill the goal of machine translation “translation without losing meaning”, a lot of translation methods have been proposed. All of these translation methods differ in their theories and implementation strategies. Although some basic rules of translation are same but many of them vary with the selection of language pair. While concerning with the scientific text, every science domain has thousands of terminologies. Translation of these terminologies according to the domain boosts the performance of translation. Translation of scientific text is ignored in the literature, as it needs more effort and expertise of both domain and language are required. In this research, we have proposed an effective scientific text translator for English to Urdu to cope with the challenge of scientific text translation. This method tags and translate the terms according to the domain. We have introduced a term tagger for tagging terms. The system can work for any domain but for experimental purpose we have selected the domain of computer science. System is evaluated on self-generated corpus of computer science. It is also compared with the existing translators to demonstrate the dominance of proposed translator as compared to the competitor. The comparative results of proposed approach and existing are shown in the form of tables.
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hal-01821049 , version 1 (22-06-2018)

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Irsha Tehseen, Ghulam Rasool Tahir, Khadija Shakeel, Mubbashir Ali. Corpus Based Machine Translation for Scientific Text. 14th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), May 2018, Rhodes, Greece. pp.196-206, ⟨10.1007/978-3-319-92007-8_17⟩. ⟨hal-01821049⟩
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