Attention based English to Punjabi neural machine translation

Abstract

Neural machine translation is an approach to learn automatic translation using a large, single neural network. It models the whole translation process in an end-to-end manner without requiring any additional components as in statistical machine translation systems. Neural machine translation has achieved promising translation performances. It has become the conventional approach in machine translation research nowadays. In this work, we applied neural machine translation for English-Punjabi language pair. In particular, attention based mechanism was used for developing the machine translation system. We also developed the parallel corpus for English-Punjabi language pair. As of now, we are releasing version-1 of the corpus and it is freely available for any non-commercial research. To the best of author’s knowledge, there is no relevant literature on neural/statistical machine translation implementation for English-Punjabi language pair as of this writing. To evaluate the system, BLEU evaluation metric was used. To quantify system’s performance, the results obtained were further compared with existing systems such as AnglaMT and Google Translate. The BLEU score of the developed system exceeds both of these systems marginally.