Hindi to English transliteration using multilayer gated recurrent units

Abstract

Transliteration is <span lang="EN-US">the task of translating text from source script to target script provided that the language of the text remains the same. In this work, we perform transliteration on less explored Devanagari to Roman Hindi transliteration and its back transliteration. The neural transliteration model in this work is based on a sequence-to-sequence neural network that is composed of two major components, an encoder that transforms source language words into a meaningful representation and the decoder that is responsible for decoding the target language words. We utilize gated recurrent units (GRU) to design the multilayer encoder and decoder network. Among the several models, the multilayer model shows the best performance in terms of coupon equivalent rate (CER) and word error rate (WER). The method generates quite satisfactory predictions in Hindi-English bilingual machine transliteration with WER of 64.8% and CER of 20.1% which is a significant improvement over existing methods.</span>

Improving the accuracy of recurrent neural networks models in predicting software bug based on undersampling methods

“…Within the GRU network, the update gate assumes a crucial role in determining the extent to which past information should be retained and carried forward to the future. Conversely, the reset gate assists in determining the degree to which past information should be ignored or forgotten [22], [23]. The calculation of the update gate in the GRU network can be demonstrated by the (6).…”