Language understanding and subsequential transducer learning

Language Understanding can be considered as the realization of a mapping from sentences of a natural language into a description of their meaning in an appropriate formal language. Under this viewpoint, the application of the Onward Subsequential Transducer Inference Algorithm (OSTIA) to Language Understanding is considered. The basic version of OSTIA is reviewed and a new version is presented in which syntactic restrictions of the domain and/or range of the target transduction can effectively be taken into account. For experimentation purposes, a task proposed by Feldman, Lakoff, Stolcke and Weber (1990) (International Computer Science Institute, Berkley, California) for assessing the capabilities of language learning and understanding systems has been adopted and three semantic coding schemes have been defined for this task with different sources of difficulty. In all cases the basic version of OSTIA has proved consistently to be able to learn very compact and accurate transducers from relativly small training sets of input–output examples of the task. Moreover, if the input sentences are corrupted with syntactic incorrectness or errors, the new version of OSTIA still provides understandable results that only degrade in a gradual and natural way.