A Neural based Bidirectional MT System to Investigate the Performance of the Low Resource Language pair English-Nepali
Main Article Content
Abstract
Machine Translation (MT), which was formerly pre-dominated by Statistical Machine Translation (SMT) and Rule-based Machine Translation (RBMT) has recently been losing its edge to the latest trends and technology, such as Neural Machine Translation (NMT) systems. Although Neural Machine Translation performs well for resource-rich languages, SMT is preferable for low-resource languages such as Nepali. Nepali on its part has unique linguistic attributes, properties and scripts. In this paper, a bidirectional SMT system for Nepali-English, a low-resource language pair, is presented. The system is built using a parallel text corpus of more than 17000 sentences and an open-source MOSES tool. Further, automatic evaluation metrics BLEU, F-Score and METEOR, are carried out to assess the effectiveness of our MT system. The system achieved scores of 21.13, 53.32 and 38.29 scores for English-Nepali and 22.26, 57.52 and 27.81 for the Nepali-English language pair respectively.
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