A Novel Machine Translation Method based on Stochastic Finite Automata Model for Spoken English

Huiyan Li

Abstract


Stochastic finite automata have been applied to a variety of fields, machine trans-lation is one of them. It can learn from data and build model automatically from training sets. Stochastic finite automata are well adapted for the constrained inte-gration of pairs of sentences for language processing. In this paper, a novel ma-chine translation method based on stochastic finite automata is proposed. The method formalized rational grammars by using stochastic finite automata. Through given pairs of source and target utterances, our proposed method will produce a series of conventional rules from which a stochastic rational grammar would be inferred, and the grammar is finally converted into a finite state automa-ton. The efficacy and accuracy of our proposed method is evaluated by a large number of English-Chinese and Chinese-English machine translation experi-ments.

Keywords


finite state models; machine translation; rational grammars; stochastic finite au-tomata

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Copyright (c) 2019 Huiyan Li


International Journal of Emerging Technologies in Learning (iJET) – eISSN: 1863-0383
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