Abstract

Corpus driven machine translation approaches such as Phrase-Based Statistical Machine Translation and Example-Based Machine Translation have been successful by using word alignment to find translation fragments for matched source parts in a bilingual training corpus. However, they still cannot properly deal with systematic translation for insertion or deletion words between two distant languages. In this work, we used syntactic chunks as translation units to alleviate this problem, improve alignments and show improvement in BLEU for Korean to English and Chinese to English translation tasks.

Keywords:
Machine translation Translation (biology) Example-based machine translation Evaluation of machine translation Machine translation software usability Transfer-based machine translation BLEU

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Topics

Legal and Regulatory Analysis
Social Sciences →  Social Sciences →  Transportation
Ego Development and Educational Practices
Social Sciences →  Psychology →  Clinical Psychology
Socioeconomic and Demographic Analysis
Physical Sciences →  Environmental Science →  Management, Monitoring, Policy and Law

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