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One model for the learning of language.
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In: Proceedings of the National Academy of Sciences of the United States of America, vol 119, iss 5 (2022)
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Abstract:
A major goal of linguistics and cognitive science is to understand what class of learning systems can acquire natural language. Until recently, the computational requirements of language have been used to argue that learning is impossible without a highly constrained hypothesis space. Here, we describe a learning system that is maximally unconstrained, operating over the space of all computations, and is able to acquire many of the key structures present in natural language from positive evidence alone. We demonstrate this by providing the same learning model with data from 74 distinct formal languages which have been argued to capture key features of language, have been studied in experimental work, or come from an interesting complexity class. The model is able to successfully induce the latent system generating the observed strings from small amounts of evidence in almost all cases, including for regular (e.g., an , [Formula: see text], and [Formula: see text]), context-free (e.g., [Formula: see text], and [Formula: see text]), and context-sensitive (e.g., [Formula: see text], and xx) languages, as well as for many languages studied in learning experiments. These results show that relatively small amounts of positive evidence can support learning of rich classes of generative computations over structures. The model provides an idealized learning setup upon which additional cognitive constraints and biases can be formalized.
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Keyword:
computational linguistics; formal language theory; Humans; Language; Learning; learning theory; Linguistics; program induction
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URL: https://escholarship.org/uc/item/6sb6g4gx
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How Efficiency Shapes Human Language
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In: https://hal.archives-ouvertes.fr/hal-03552539 ; 2022 (2022)
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One model for the learning of language
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In: Proc Natl Acad Sci U S A (2022)
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Variation in spatial concepts: Different frames of reference on different axes
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In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol 43, iss 43 (2021)
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Variation in spatial concepts: Different frames of reference on different axes ...
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The Natural Stories corpus: a reading-time corpus of English texts containing rare syntactic constructions
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In: Springer Netherlands (2020)
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Recursive sequence generation in monkeys, children, U.S. adults, and native Amazonians
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In: Sci Adv (2020)
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Composition is the core driver of the language-selective network
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In: MIT Press (2019)
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How Efficiency Shapes Human Language ; How Efficiency Shapes Human Language, TICS 2019
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In: Prof. Levy via Courtney Crummett (2019)
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Post Hoc Analysis Decisions Drive the Reported Reading Time Effects in Hackl, Koster-Hale & Varvoutis (2012)
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In: Other repository (2019)
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Table of assumptions used in our estimates from Humans store about 1.5 megabytes of information during language acquisition ...
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Table of assumptions used in our estimates from Humans store about 1.5 megabytes of information during language acquisition ...
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Supplementary material from "Humans store about 1.5 megabytes of information during language acquisition" ...
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Supplementary material from "Humans store about 1.5 megabytes of information during language acquisition" ...
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18 |
Word Forms Are Structured for Efficient Use
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In: ISSN: 0364-0213 ; EISSN: 1551-6709 ; Cognitive Science ; https://hal.archives-ouvertes.fr/hal-03552561 ; Cognitive Science, Wiley, 2018, 42 (8), pp.3116-3134. ⟨10.1111/cogs.12689⟩ (2018)
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Word Forms Are Structured for Efficient Use
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In: Prof. Gibson via Courtney Crummett (2018)
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20 |
Color naming across languages reflects color use
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In: National Academy of Sciences (2018)
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