Experiments Using OSTIA for a Language Production Task

The phenomenon of meaning-preserving corrections given by an adult to a child involves several aspects: (1) the child produces an incorrect utterance, which the adult nevertheless understands, (2) the adult produces a correct utterance with the same meaning and (3) the child recognizes the adult utterance as having the same meaning as its previous utterance, and takes that as a signal that its previous utterance is not correct according to the adult grammar. An adequate model of this phenomenon must incorporate utterances and meanings, account for how the child and adult can understand each other’s meanings, and model how meaning-preserving corrections interact with the child’s increasing mastery of language production. In this paper we are concerned with how a learner who has learned to comprehend utterances might go about learning to produce them. We consider a model of language comprehension and production based on finite sequential and subsequential transducers. Utterances are modeled as finite sequences of words and meanings as finite sequences of predicates. Comprehension is interpreted as a mapping of utterances to meanings and production as a mapping of meanings to utterances. Previous work (Castellanos et al., 1993; Pieraccini et al., 1993) has applied subsequential transducers and the OSTIA algorithm to the problem of learning to comprehend language; here we apply them to the problem of learning to produce language. For ten natural languages and a limited domain of geometric shapes and their properties and relations we define sequential transducers to produce pairs consisting of an utterance in that language and its meaning. Using this data we empirically explore the properties of the OSTIA and DD-OSTIA algorithms for the tasks of learning comprehension and production in this domain, to assess whether they may provide a basis for a model of meaning-preserving corrections.