Improving the Quality of MT Output using Novel Name Entity Translation Scheme
This work addresses a domain-specific issue in machine translation for Punjabi language users, but it is incremental as it builds on existing name entity translation schemes.
The paper tackled the problem of improper name entity translation degrading machine translation quality by developing a statistical rule-based approach for transliterating name entities from English to Punjabi, achieving transliteration through probability calculations using the MOSES toolkit.
This paper presents a novel approach to machine translation by combining the state of art name entity translation scheme. Improper translation of name entities lapse the quality of machine translated output. In this work, name entities are transliterated by using statistical rule based approach. This paper describes the translation and transliteration of name entities from English to Punjabi. We have experimented on four types of name entities which are: Proper names, Location names, Organization names and miscellaneous. Various rules for the purpose of syllabification have been constructed. Transliteration of name entities is accomplished with the help of Probability calculation. N-Gram probabilities for the extracted syllables have been calculated using statistical machine translation toolkit MOSES.