SKATE: A Natural Language Interface for Encoding Structured Knowledge

• URL: http://arxiv.org/abs/2010.10597v2
• Date: Fri, 11 Dec 2020 01:01:45 GMT
• Title: SKATE: A Natural Language Interface for Encoding Structured Knowledge
• Authors: Clifton McFate, Aditya Kalyanpur, Dave Ferrucci, Andrea Bradshaw, Ariel Diertani, David Melville, Lori Moon
• Abstract summary: 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.
• Score: 3.7296147370114183
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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