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Computer Science > Computation and Language

arXiv:2206.14672 (cs)
[Submitted on 29 Jun 2022 (v1), last revised 17 Nov 2022 (this version, v4)]

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

Authors:Lize Alberts
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Abstract:Recent hype surrounding the increasing sophistication of language processing models has renewed optimism regarding machines achieving a human-like command of natural language. Research in the area of natural language understanding (NLU) in artificial intelligence claims to have been making great strides in this area, however, the lack of conceptual clarity/consistency in how 'understanding' is used in this and other disciplines makes it difficult to discern how close we actually are. In this interdisciplinary research thesis, I integrate insights from cognitive science/psychology, philosophy of mind, and cognitive linguistics, and evaluate it against a critical review of current approaches in NLU to explore the basic requirements--and remaining challenges--for developing artificially intelligent systems with human-like capacities for language use and comprehension.
Comments: Philosophy master's thesis (2020) available on the SUNScholar research repository (this https URL)
Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
MSC classes: 68T01
ACM classes: F.0; J.0; J.4; I.2
Cite as: arXiv:2206.14672 [cs.CL]
  (or arXiv:2206.14672v4 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2206.14672

Submission history

From: Lize Alberts [view email]
[v1] Wed, 29 Jun 2022 14:19:48 UTC (831 KB)
[v2] Fri, 1 Jul 2022 11:22:50 UTC (815 KB)
[v3] Mon, 4 Jul 2022 08:23:28 UTC (815 KB)
[v4] Thu, 17 Nov 2022 10:49:29 UTC (1,969 KB)
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