A fully integrated approach to Speech-Input Language Translation in limited-domain applications is presented. The mapping from the input to the output language is modeled in terms of a finite state translation model which is learned from examples of input-output sentences of the task considered. This model is tightly integrated with standard acoustic-phonetic models of the input language and the resulting global model directly supplies, through Viterbi search, an optimal output-language sentence for each input -language utterance. Several extensions to this framework, recently developed to cope with the increasing difficulty of translation tasks, are reviewed. Finally, results for a task in the framework of hotel front-desk communication, with a vocabulary of about 700 words, are reported.

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 et al. for assessing the capabilities of Language Learning and Understanding systems has been adopted and three increasingly difficult-tolearn semantic coding schemes have been defined for this task. In all cases the basic version of OSTIA has consistently proved able to learn very compact and accurate transducers from relatively 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 understanding results that only degrade in a gradual and natural way.

We present a review of some recently developed techniques in the field of natural language processing. This area has witnessed a confluence of approaches which are inspired by theories from linguistics and those which are inspired by theories from information theory: statistical language models are becoming more linguistically sophisticated and the models of language used by linguists are incorporating stochastic techniques to help resolve ambiguities. We include a discussion about the underlying similarities between some of these systems and mention two approaches to the evaluation of statistical language processing systems. 1 Introduction Within the last decade, a great deal of attention has been paid to techniques for processing large natural language copora. The purpose of much of this activity has been to refine computational models of language so that the performance of various technical applications can be improved (e.g. speech recognisers [67], speech synthesisers [32], optica...

Language Understanding in limited domains is here approached as a problem of language translation in which the target language is a formal language rather than a natural one. Finite-state transducers are used to model the translation process. Furthermore, these models are automatically learned from training data consisting of pairs of natural-language/formal-language sentences. The need for training data is dramatically reduced by performing a two-step learning process based on lexical/phrase categorization.

The interest in using Finite-State Models in a large variety of applications is recently growing as more powerful techniques for learning them from examples have been developed. Language Understanding can be approached this way as a problem of language translation in which the target language is a formal language rather than a natural one. Finite-state transducers are used to model the translation process, and are automatically learned from training data consisting of pairs of natural-language/formal-language sentences. The need for training data is dramatically reduced by performing a two-level learning process based on lexical/phrase categorization. Successful experiments are presented on a task consisting in the "understanding" of Spanish natural-language sentences describing dates and times, where the target formal language is the one used in the popular Unix command "at".

The use of Subsequential Transducers (a kind of FiniteState Models) in Automatic Translation applications is considered. A methodology that improves the performance of the learning algorithm by means of an automatic reordering of the output sentences is presented. This technique yields a greater degree of synchrony between the input and output samples. The proposedapproachleads to a reduction in the number of samples necessary to learn the transducer and a reduction in the size of the model so obtained.