Language Generation for Broad-Coverage, Explainable Cognitive Systems
This work addresses language generation for explainable cognitive systems, but it is incremental as it builds directly on prior understanding methods.
The paper tackles natural language generation for intelligent agents by extending an existing cognitive architecture's understanding framework to generation, achieving broad-coverage capabilities while supporting near-term applications.
This paper describes recent progress on natural language generation (NLG) for language-endowed intelligent agents (LEIAs) developed within the OntoAgent cognitive architecture. The approach draws heavily from past work on natural language understanding in this paradigm: it uses the same knowledge bases, theory of computational linguistics, agent architecture, and methodology of developing broad-coverage capabilities over time while still supporting near-term applications.