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Learning finite-state models for machine translation
2006
Machine Learning
In formal language theory, finite-state transducers are well-know models for simple "input-output" mappings between two languages. Even if more powerful, recursive models can be used to account for more complex mappings, it has been argued that the input-output relations underlying most usual natural language pairs can essentially be modeled by finitestate devices. Moreover, the relative simplicity of these mappings has recently led to the development of techniques for learning finite-state
doi:10.1007/s10994-006-9612-9
fatcat:2brzvkrv4jg7dhivheufceltya