Stone Soup Translation: the Linked Automata Model
نویسندگان
چکیده
The automated translation of one natural language to another, known as machine translation (MT), typically requires successful modeling of the grammars of the languages and the relationship between them. Rather than hand-coding these grammars and relationships, some machine translation efforts employ data-driven methods, where the goal is to learn from a large amount of training examples of accurate translations. One such data-driven approach is statistical MT, where language and alignment models are automatically induced from parallel corpora. This work has also been extended to probabilistic finite-state approaches, most often via transducers. This dissertation introduces and begins an investigation of an MT model consisting of a novel combination finite-state devices. The model proposed is more flexible than transducer models, giving increased ability to handle word order differences between languages, as well as crossing and discontinuous alignments between words. The linked automata MT model consists of a source language automaton, a target language automaton, and an alignment table—a function which probabilistically links sequences of source and target language transitions. It is this augmentation to the finite-state base which gives the linked automata model its flexibility. The dissertation describes the linked automata model from the ground up, beginning with a description of some of the relevant MT history and empirical MT
منابع مشابه
Stone Soup Translation ∗
The automated translation of one natural language to another, known as machine translation, typically requires successful modeling of the grammars of the two languages and of the relationship between them. Rather than hand-coding these grammars and relationships, some machine translation efforts have begun to employ statistical methods, where the goal is to learn from a large amount of training...
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