Poster: ProNat: An Agent-Based System Design for Programming in Spoken Natural Language

摘要

The emergence of natural language interfaces has led to first attempts of programming in natural language. We present ProNat, a tool for script-like programming in spoken natural language (SNL). Its agent-based architecture unifies deep natural language understanding (NLU) with modular software design. ProNat focuses on the extraction of processing flows and control structures from spoken utterances. For evaluation we have begun to build a speech corpus. First experiments are conducted in the domain of domestic robotics, but ProNat's architecture makes domain acquisition easy. Test results with spoken utterances in ProNat seem promising, but much work has to be done to achieve deep NLU.