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.
@article{arxiv.2201.10422,
title = {Language Generation for Broad-Coverage, Explainable Cognitive Systems},
author = {Marjorie McShane and Ivan Leon},
journal= {arXiv preprint arXiv:2201.10422},
year = {2022}
}
Comments
Presented at The Ninth Advances in Cognitive Systems (ACS) Conference 2021 (arXiv:2201.06134)