A Fully Statistical Approach to Natural Language Interfaces

A FULLY STATISTICAL APPROACH TO NATURAL LANGUAGE INTERFACES Scott Miller, David Stallard, Robert Bobrow, Richard Schwartz BBN Systems and Technologies 70 Fawcett Street Cambridge, MA 02138 szmiller@bbn.com, stallard@bbn.com, rusty@bbn.com, schwartz@bbn.com Abstract We present a natural language interface system which is based entirely on trained statistical models. The system consists of three stages of processing: parsing, semantic interpretation, and discourse. Each of these stages is modeled as a statistical process. The models are fully integrated, resulting in an end-to-end system that maps input utterances into meaning representation frames. 1. Introduction A recent trend in natural language processing has been toward a greater emphasis on statistical approaches, beginning with the success of statistical part-of-speech tagging programs (Church 1988), and continuing with other work using statistical part-of-speech tagging programs, such as BBN PLUM (Weischedel et al. 1993) and NYU Proteus (Grishman and Sterling 1993). More recently, statistical methods have been applied to domain-specific semantic parsing (Miller et al. 1994), and to the more difficult problem of wide-coverage syntactic parsing (Magerman 1995). Nevertheless, most natural language systems remain primarily rule based, and even systems that do use statistical techniques, such as AT&T Chronus (Levin and Pieraccini 1995), continue to require a significant rule based component. Development of a complete end-to-end statistical understanding system has been the focus of several ongoing research efforts, including (Miller et al. 1995) and (Koppelman et al. 1995). In this paper, we present such a system. The overall structure of our approach is conventional, consisting of a parser, a semantic interpreter, and a discourse module. The implementation and integration of these elements is far less conventional. Within each module, every processing step is assigned a probability value, and very large numbers of alternative theories are pursued in parallel. The individual modules are integrated through an n-best paradigm, in which many theories are passed from one stage to the next, together with their associated probability scores. The meaning of a sentence is determined by taking the highest scoring theory from among the n-best possibilities produced by the final stage in the model. Some key advantages to statistical modeling techniques are: • All knowledge required by the system is acquired through training examples, thereby eliminating the need for hand-written rules. In parsing for example, it is sufficient to provide the system with examples specifying the correct parses for a set of training examples. There is no need to specify an exact set of rules or a detailed procedure for producing such parses. • All decisions made by the system are graded, and there are principled techniques for estimating the gradations. The system is thus free to pursue unusual theories, while remaining aware of the fact that they are unlikely. In the event that a more likely theory exists, then the more likely theory is selected, but if no more likely interpretation can be found, the unlikely interpretation is accepted. The focus of this work is primarily to extract sufficient information from each utterance to give an appropriate response to a user's request. …

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