Source side pre-ordering using recurrent neural networks for English-Myanmar machine translation

May Kyi Nyein, Khin Mar Soe


Word reordering has remained one of the challenging problems for machine translation when translating between language pairs with different word orders e.g. English and Myanmar. Without reordering between these languages, a source sentence may be translated directly with similar word order and translation can not be meaningful. Myanmar is a subject-object-verb (SOV) language and an effective reordering is essential for translation. In this paper, we applied a pre-ordering approach using recurrent neural networks to pre-order words of the source Myanmar sentence into target English’s word order. This neural pre-ordering model is automatically derived from parallel word-aligned data with syntactic and lexical features based on dependency parse trees of the source sentences. This can generate arbitrary permutations that may be non-local on the sentence and can be combined into English-Myanmar machine translation. We exploited the model to reorder English sentences into Myanmar-like word order as a pre-processing stage for machine translation, obtaining improvements quality comparable to baseline rule-based pre-ordering approach on asian language treebank (ALT) corpus.


asian language treebank; dependency parse trees; machine translation; pre-ordering model; recurrent neural networks; word reordering;


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International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578