Learning finite-state models for machine translation

Francisco Casacuberta, Enrique Vidal
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
more » ... sducers from a training set of inputoutput sentence pairs of the languages considered. In the last years, these techniques have lead to the development of a number of machine translation systems. Under the statistical statement of machine translation, we overview here how modeling, learning and search problems can be solved by using stochastic finite-state transducers. We also review the results achieved by the systems we have developed under this paradigm. As a main conclusion of this review we argue that, as task complexity and training data scarcity increase, those systems which rely more on statistical techniques tend produce the best results.
doi:10.1007/s10994-006-9612-9 fatcat:2brzvkrv4jg7dhivheufceltya