Neural Machine Transliteration: Preliminary Results
This addresses the problem of script transformation for language processing, but it is incremental as it applies an existing sequence-to-sequence paradigm to transliteration.
The paper tackled machine transliteration by proposing a character-based encoder-decoder model using recurrent neural networks and attention, achieving significantly higher transliteration quality over traditional statistical models.
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.