Toward an inference procedure by type checking algorithm for Neural DTS

Abstract

The semantics of natural language based on dependent type theory has provided a method for recognizing textual entailment, and type checking algorithms underlying such recognizing textual entailment systems are formulated for systems consisting of symbolic reasoning. Recently, systems with embedded neural networks have been proposed for natural language semantics based on dependent type theory. Neural DTS, which is derived from Dependent Type Semantics (DTS), is one such system.In this study, by defining a type checking algorithm for Neural DTS, we formulate a neural inference procedure for the propositions composed from atomic relational propositions and their negations by conjunction and disjunction. First, a classifier is trained on a dataset extracted from WordNet. Next, we embed the trained classifier in a type checking algorithm, replacing binary relation symbols with it. We then show that prediction concerning the propositions mentioned above can be made by means of type checking for these propositions.