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Language Generation for Broad-Coverage, Explainable Cognitive Systems

arXiv 2201.10422 ↗First posted 25 Jan 2022cs.CL cs.AIcited by 0

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Abstract (arXiv)

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.

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References · 7

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2021Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsitycited by 3,014
  3. 2018Wizard of Wikipedia: Knowledge-Powered Conversational agentscited by 530
  4. 2017Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluationcited by 206
  5. 2020You Impress Me: Dialogue Generation via Mutual Persona Perceptioncited by 85
  6. 2019A Survey of Natural Language Generation Techniques with a Focus on Dialogue Systems - Past, Present and Future Directionscited by 26
  7. 2020Multi-turn Response Selection using Dialogue Dependency Relationscited by 23

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