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Recent hype surrounding the increasing sophistication of language processingmodels has renewed optimism regarding machines achieving a human-like commandof natural language. Research in the area of natural language understanding(NLU) in artificial intelligence claims to have been making great strides inthis area, however, the lack of conceptual clarity/consistency in how'understanding' is used in this and other disciplines makes it difficult todiscern how close we actually are. In this interdisciplinary research thesis, Iintegrate insights from cognitive science/psychology, philosophy of mind, andcognitive linguistics, and evaluate it against a critical review of currentapproaches in NLU to explore the basic requirements--and remainingchallenges--for developing artificially intelligent systems with human-likecapacities for language use and comprehension.

Not Cheating on the Turing Test: Towards Grounded Language Learning in Artificial Intelligence