Enhancing Medium-Sized Sentence Translation In English-Hindi NMT Using Clause-Based Approach
Saket Thakur, Jyoti Srivastava · 2024
The issues of structural divergence, ambiguity, low resource language, and lack of training data are present in neural machine translation from English-to-Hindi and Hindi-to-English. This paper explores a comprehensive approach to tackle these challenges in English-to-Hindi and Hindi-to-English neural machine translation. Through a thorough examination of the identified challenges and the proposed solutions, with consideration for the nuances of Hindi language usage and structure, this research aims to open the path for more reliable and accurate machine translation systems tailored to the nuances of Hindi language structure and usage [1]. This study presents the work on English-Hindi Parallel Corpus developed by IIT Bombay. This study reports an updated baseline NMT translation result and a clause-based NMT translation result on this corpus using open-source NMT. Prior to translation, this study focuses on breaking down longer input sentences into smaller clauses hoping that it may increase the quality of the translation and lessen the likelihood that crucial context will be lost in the process. An experiment with different clause lengths is done to find the sweet spot for this model and analyze how they affect translation quality. The final results are obtained by using Open NMT which gives approx 3% increase for English to Hindi translation and significant improvement is achieved for Hindi to English translation [2].