Improving the quality of Gujarati-Hindi Machine Translation through part-of-speech tagging and stemmer-assisted transliteration
This work addresses translation quality for Gujarati-Hindi language pairs, which is an incremental improvement in a domain-specific area of Indian language machine translation.
The paper tackled the problem of low-quality Gujarati-Hindi machine translation by proposing a method using part-of-speech tagging and stemming to improve transliteration, resulting in enhanced translation efficiency as demonstrated through content analysis.
Machine Translation for Indian languages is an emerging research area. Transliteration is one such module that we design while designing a translation system. Transliteration means mapping of source language text into the target language. Simple mapping decreases the efficiency of overall translation system. We propose the use of stemming and part-of-speech tagging for transliteration. The effectiveness of translation can be improved if we use part-of-speech tagging and stemming assisted transliteration.We have shown that much of the content in Gujarati gets transliterated while being processed for translation to Hindi language.