Growing Semantic Grammars

Abstract

A critical path in the development of natural language understanding (NLU) modules lies in the difficulty of defining a mapping from words to semantics: Usually it takes in the order of years of highly-skilled labor to develop a semantic mapping, e.g., in the form of a semantic grammar, that is comprehensive enough for a given domain. Yet, due to the very nature of human language, such mapping invariably fail to achieve full coverage on unseen data. Acknowledging the impossibility of stating a priori all the surface forms by which a concept can be expressed, we present GSG: an empathic computer system for the rapid deployment of NLU front-ends and their dynamic customization by non-expert end-users. Given a new domain for which an NLU front-end is to be developed, two stages are involved. In the authoring stage, GSG aids the developer in the construction of a simple domain model and a kernel analysis grammar. Then, in the run-time stage, GSG provides the end-user with an interactive environment in which the kernel grammar is dynamically extended. Three learning methods are employed in the acquisition of semantic mappings from unseen data: (i) parser predictions, (ii) hidden understanding model, and (iii) end-user paraphrases. A baseline version of GSG has been implemented and preliminary experiments show promising results.