From Natural Language Instructions to Complex Processes: Issues in Chaining Trigger Action Rules
This addresses the need for natural language interfaces to enable general users to automate complex business processes, but it appears incremental as it builds on existing semantic parsing methods.
The paper tackles the problem of semantic parsing for complex workflows in intelligent process automation by defining a new grammar for chaining machine-executable meaning representations and proposing a dataset creation approach, but does not report concrete results or numbers.
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.