Learning to translate: a psycholinguistic approach to the induction of grammars and transfer functions
Patrick Juola · 1996
dentified many constraints on the form and processing of human languages. By incorporating these constraints into a language learning system, it is possible to build a system that learns to translate (infers functions and grammars for machine translation) from an aligned bilingual corpus of sentences using understandable, symbolic linguistic principles and representations. This work focuses on one particular constraint, the Marker Hypothesis, which is shown to be powerful, understandable, and computationally accessible. This hypothesis has been incorporated into a family of systems that infer such transfer functions using standard multivariate optimization techniques. These systems have been tested on a variety of language pairs and corpora, demonstrating the language and corpus independence of this approach. Furthermore, the design iv principles are in theory independent of any particular inference technique or grammatical representation and reflect only the constraints of the Marke