Neural Machine Transliteration: Preliminary Results

Amir H. Jadidinejad

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arXiv · 1609.04253

Neural Machine Transliteration: Preliminary Results

Amir H. Jadidinejad

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Abstract

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.

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BibTeX RIS

Amir H. Jadidinejad. 2016-09-14. Neural Machine Transliteration: Preliminary Results. https://arxiv.org/abs/1609.04253

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