An Enhanced RBMT: When RBMT Outperforms Modern Data-Driven Translators.

Although prominent translators, such as Google, Yahoo Babel Fish, Bing, etc., perform better when translating most widely used languages, they tend to commit fundamental mistakes in working with low-resource languages such as Bengali, Romanian, Arabic, etc. Such translators (e.g. Google Translate) use different data-driven translation approaches, such as neural machine translation (NMT), statistical machine translation (SMT), etc., to develop their polyglot translation system. However, performances of these data-driven approaches entirely rely on the attainability of significantly large parallel corpora of the translating language pairs. As a consequence, numerous popular languages, such as Bengali, remain barely explored not only in machine translation but also in other fields of natural language processing. Therefore, the target of this study is to explore effective translation from Bengali to English by accomplishing several Bengali language processing tasks. To be precise, we adopt a basic rule-based machine translator for translating from Bengali to English. Next, we enhance its performance by considering the veracious interpretation of the Bengali names as subjects (and nouns) in a sentence. Besides, we propose a Bengali verb identification and optimization technique by root-word detection (stemming) of the Bengali verbs. Finally, we unfold the efficacy of our proposed techniques through a comparative analysis with popular data-driven translators using a novel customized dataset focusing on Bengali-to-English translation. [ABSTRACT FROM AUTHOR]