SKATE: A Natural Language Interface for Encoding Structured Knowledge
35th AAAI Conference on Artificial Intelligence / 33rd Conference on Innovative Applications of Artificial Intelligence / 11th Symposium on Educational Advances in Artificial Intelligence
; 35:15362-15369, 2021.
Article
in English
| Web of Science | ID: covidwho-1436830
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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Collection:
Databases of international organizations
Database:
Web of Science
Language:
English
Journal:
33rd Conference on Innovative Applications of Artificial Intelligence
Year:
2021
Document Type:
Article
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