# Knowledge Engineering in the Long Game of Artificial Intelligence: The Case of Speech Acts

arXiv: https://arxiv.org/abs/2202.01040 · HTML: https://mlsanity.com/paper/2202.01040/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 24 references that are in the map.
- First posted on arXiv 2 Feb 2022.

## Builds on (most cited first, 1 of 1)

- [Deep Learning: A Critical Appraisal](https://mlsanity.com/paper/1801.00631/) (2018)

## Built on by (most cited first, 1 of 1)

- [The Path to Autonomous Learners](https://mlsanity.com/paper/2211.02403/) (2022)

## Abstract (arXiv)

This paper describes principles and practices of knowledge engineering that enable the development of holistic language-endowed intelligent agents that can function across domains and applications, as well as expand their ontological and lexical knowledge through lifelong learning. For illustration, we focus on dialog act modeling, a task that has been widely pursued in linguistics, cognitive modeling, and statistical natural language processing. We describe an integrative approach grounded in the OntoAgent knowledge-centric cognitive architecture and highlight the limitations of past approaches that isolate dialog from other agent functionalities.