Linguistics for the Age of AI
A human-inspired, linguistically sophisticated model of language understanding for intelligent agent systems. One of the original goals of artificial intelligence research was to endow intelligent agents with human-level natural language capabilities. Recent AI research, however, has focused on applying statistical and machine learning approaches to big data rather than attempting to model what people do and how they do it. In this book, Marjorie McShane and Sergei Nirenburg return to the original goal of recreating human-level intelligence in a machine. They present a human-inspired, linguistically sophisticated model of language understanding for intelligent agent systems that emphasizes meaning—the deep, context-sensitive meaning that a person derives from spoken or written language. With Linguistics for the Age of AI, McShane and Nirenburg offer a roadmap for creating language-endowed intelligent agents (LEIAs) that can understand,explain, and learn. They describe the language-understanding capabilities of LEIAs from the perspectives of cognitive modeling and system building, emphasizing “actionability”—which involves achieving interpretations that are sufficiently deep, precise, and confident to support reasoning about action. After detailing their microtheories for topics such as semantic analysis, basic coreference, and situational reasoning, McShane and Nirenburg turn to agent applications developed using those microtheories and evaluations of a LEIA's language understanding capabilities. McShane and Nirenburg argue that the only way to achieve human-level language understanding by machines is to place linguistics front and center, using statistics and big data as contributing resources. They lay out a long-term research program that addresses linguistics and real-world reasoning together, within a comprehensive cognitive architecture.
Keywordsnatural language understanding; computational semantics; computational pragmatics; computational linguistics; intelligent agents; cognitive modelling; cognitive systems; AI; artificial intelligence; language-endowed intelligent agents; natural language processing; NLP; language-endowed intelligent agent systems; linguistic and extralinguistic scope; understanding; Extracting and representing meaning; theories; systems and models; actionability; explanation; Theory and methodology; knowledge bases; incrementality; microtheories; Pre-semantic analysis; error recovery; managing complexity; Modification; proposition-level semantic enhancements; constructions; indirect speech acts; non-literal language; ellipsis; fragments; unknown words; personal pronouns; broad referring expressions; definite descriptions; anaphoric event coreference; Residual ambiguities; incongruities; underspecification; incorporating; OntoAgent cognitive architecture; fractured syntax; treating underspecified elements; Integrated NLU applications; Maryland Virtual Patient; cognitive robotics; Model and system evaluation; component-level evaluation; holistic evaluation
PublisherThe MIT Press
Publication date and placeCambridge, 2021
ImprintThe MIT Press
SeriesThe MIT Press,