SKATE: A Natural Language Interface for Encoding Structured Knowledge

Authors

  • Clifton McFate Elemental Cognition
  • Aditya Kalyanpur ElementalCognition
  • Dave Ferrucci Elemental Cognition
  • Andrea Bradshaw Elemental Cognition
  • Ariel Diertani Elemental Cognition
  • David Melville Elemental Cognition
  • Lori Moon Elemental Cognition

Keywords:

Knowledge Capture, Semantic Parsing, Frame Semantics

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 technology in a restricted domain: COVID-19 policy design.

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Published

2021-05-18

How to Cite

McFate, C., Kalyanpur, A., Ferrucci, D., Bradshaw, A., Diertani, A., Melville, D., & Moon, L. (2021). SKATE: A Natural Language Interface for Encoding Structured Knowledge. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 15362-15369. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17804

Issue

Section

IAAI Technical Track on Emerging Applications of AI