Transducer learning in pattern recognition

Abstract

'Interpretation' is a general and interesting pattern recognition framework in which a system is considered to input object representations, and output the corresponding interpretations in terms of 'semantic messages' specifying the actions to be carried out as system's responses. From the syntactic pattern recognition viewpoint, interpretation reduces to formal transduction. The authors propose an efficient and effective algorithm to automatically infer a finite state transducer from a training set of input-output examples of the interpretation problem considered. The proposed algorithm has been shown to identify an important class of transductions known as 'subsequential transductions.' Experimental results are presented showing the performance and capabilities of the proposed method.<>