Modularity in a Connectionist Model of Morphology Acquisition

MODULARITY IN A CONNECTIONIST MODEL MORPHOLOGY ACQUISITION Michael Gasser Departments of Computer Science and Linguistics Indiana University OF Abstract This paper describes a modular connection,st model of the acquisition of receptive inflectional morphology. The model takes inputs in the form of phones one at a time and outputs the associ- ated roots and infections. In its simplest version, the network consists of separate simple recurrent subnetworks for root and inflection identification; both networks take the phone sequence as inputs. It is shown that the performance of the two separate modular networks is superior to a single network re- sponsible for both root and inflection identification. In a more elaborate version of the model, the net- work learns to use separate hidden-layer modules to solve the separate tasks of root and inilection identification. INTRODUCTION For many natural languages, the complexity of bound morphology makes it a potentially challeng- ing problem for a learning system, wl, ether hu- man or machine. A language learner must ac- quire both the ability to map polymorphemlc words onto the sets of semantic elements they tel)resent and to map meanings onto polymorphemic words. Unlike previous work on connection,st morphology (e.g., MacWhinney ~5 Leinbaeh (1991), Plunker, & Marehman (1991) and Rumelhart & MeClelland (1986)), the focus of this paper is receptive nmr- phology, which represents the more fundamental, or at least the earlier, process, one which produc- tive nmrphology presumably buihls on. The task of learning receptive morphology is viewed here ,as follows. The learner is "trained" on pairs of forms, consisting of sequcnces of phones, and "meanings", consisting of sets of roots and in- flections. I will refer to the task as root and inflec- tion identification. Generalization is tested by pre- senting the learner with words consisting of novel combinations of familiar morphemes. If the rule in question has been acquired, the learner is able to identify the root and inflections in the test word. Of interest is whether a model is capable of ac- quiring rules of all of the types known for natural languages. This paper describes a psychologically motivated connection,st model (Modular Connec- tion,st Network for the Acquisition of Morphology, MCNAM) which approaches this level of perfor- mance. The emphasis here is on the role of mod- ularity at the level of root and inflection in the model. I show how this sort of modularity improves performance (lramatically and consider how a net- work might learn to use modules it is provided with. A sel)arate paper (Gasser, 1994) looks in detail at the model's performance for particular categories of morI)hology, in particular, template morphology and reduplication. The paper is organized as folh)ws. I first provide a brief overview of the categories of morphological rules found in the werhl's languages. I then present a simple version of the model and discuss simula- tions which demonstrate that it generalizes for most kinds of morphoh)gical rules. I then describe a ver- sion of the model augmented with modularity at the level of root and inflection …

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