English to Persian transliteration using attention-based approach in deep learning

In this paper, transliteration is carried out by using the attention-based approach in deep learning. Unlike the previous works which randomly initialize the weights in the encoder, word vector representation of the source vocabulary has been used as an initial value for the weights. The representation is computed by counting the co-occurrences between different characters. Experimental results on an English to Persian transliteration corpus with more than 14000 word pairs show the superior performance of the proposed method (up to 4.21 BLEU points improvement) over the basic attention-based approach.