Extending Machine Language Models toward Human-Level Language Understanding

McClelland, James; Hill, Felix; Rudolph, Maja; Baldridge, Jason and Schütze, Hinrich (12. December 2019): Extending Machine Language Models toward Human-Level Language Understanding.

Abstract

Language is central to human intelligence. We review recent break- throughs in machine language processing and consider what re- mains to be achieved. Recent approaches rely on domain general principles of learning and representation captured in artificial neu- ral networks. Most current models, however, focus too closely on language itself. In humans, language is part of a larger system for acquiring, representing, and communicating about objects and sit- uations in the physical and social world, and future machine lan- guage models should emulate such a system. We describe exist- ing machine models linking language to concrete situations, and point toward extensions to address more abstract cases. Human language processing exploits complementary learning systems, in- cluding a deep neural network-like learning system that learns grad- ually as machine systems do, as well as a fast-learning system that supports learning new information quickly. Adding such a system to machine language models will be an important further step toward truly human-like language understanding.

• Item Type: Paper
• EU Funded Grant Agreement Number: 740516
• EU Projects: Horizon 2020 > ERC Grants > ERC Advanced Grant > ERC Grant 740516: NonSequeToR - Non-sequence models for tokenization replacement
• Research Centers: Center for Information and Language Processing (CIS)
• Subjects: 000 Computer science, information and general works > 000 Computer science, knowledge, and systems; 400 Language > 410 Linguistics
• Language: English