From Natural Language Instructions to Complex Processes: Issues in Chaining Trigger Action Rules
Year: 2020
Type: preprint
Abstract: Automation services for complex business processes usually require a high level of information technology literacy. There is a strong demand for a smartly assisted process automation (IPA: intelligent process automation) service that enables even general users to easily use advanced automation. A natural language interface for such automation is expected as an elemental technology for the IPA realization. The workflow targeted by IPA is generally composed of a combination of multiple tasks. However, semantic parsing, one of the natural language processing methods, for such complex workflows has not yet been fully studied. The reasons are that (1) the formal expression and grammar of the workflow required for semantic analysis have not been sufficiently examined and (2) the dataset of the workflow formal expression with its corresponding natural language description required for learning workflow semantics did not exist. This paper defines a new grammar for complex workflows with chaining machine-executable meaning representations for semantic parsing. The representations are at a high abstraction level. Additionally, an approach to creating datasets is proposed based on this grammar.
Source: arXiv (Cornell University)
Authors: Ito, Nobuhiro, Suzuki, Yuya, Aizawa, Akiko
Institutions: The University of Tokyo, National Institute of Informatics
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2998773523