In Natural Language (NL) applications, there is often a mismatch between what the NL interface is capable of interpreting and what a lay user knows how to express. This work describes a novel natural language interface that reduces this mismatch by refining natural language input through successive, automatically generated semi-structured templates. In this paper we describe how our approach, called SKATE, uses a neural semantic parser to parse NL input and suggest semi-structured templates, which are recursively filled to produce fully structured interpretations. We also show how SKATE integrates with a neural rule-generation model to interactively suggest and acquire commonsense knowledge. We provide a preliminary coverage analysis of SKATE for the task of story understanding, and then describe a current business use-case of the tool in a specific domain: COVID-19 policy design.
@article{arxiv.2010.10597,
title = {SKATE: A Natural Language Interface for Encoding Structured Knowledge},
author = {Clifton McFate and Aditya Kalyanpur and Dave Ferrucci and Andrea Bradshaw and Ariel Diertani and David Melville and Lori Moon},
journal= {arXiv preprint arXiv:2010.10597},
year = {2020}
}