  Machine transliteration is the process of automatically transforming the
script of a word from a source language to a target language, while preserving
pronunciation. Sequence to sequence learning has recently emerged as a new
paradigm in supervised learning. In this paper a character-based
encoder-decoder model has been proposed that consists of two Recurrent Neural
Networks. The encoder is a Bidirectional recurrent neural network that encodes
a sequence of symbols into a fixed-length vector representation, and the
decoder generates the target sequence using an attention-based recurrent neural
network. The encoder, the decoder and the attention mechanism are jointly
trained to maximize the conditional probability of a target sequence given a
source sequence. Our experiments on different datasets show that the proposed
encoder-decoder model is able to achieve significantly higher transliteration
quality over traditional statistical models.
