A Theory of Language Learning
- URL: http://arxiv.org/abs/2106.14612v1
- Date: Sun, 6 Jun 2021 11:06:42 GMT
- Title: A Theory of Language Learning
- Authors: Robert Worden
- Abstract summary: A theory of language learning is described, which uses Bayesian induction of feature structures (scripts) and script functions.
Each word sense in a language is mentally represented by an m-script, a script function which embodies all the syntax and semantics of the word.
M-scripts form a fully-lexicalised unification grammar, which can support adult language.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: A theory of language learning is described, which uses Bayesian induction of
feature structures (scripts) and script functions. Each word sense in a
language is mentally represented by an m-script, a script function which
embodies all the syntax and semantics of the word. M-scripts form a
fully-lexicalised unification grammar, which can support adult language. Each
word m-script can be learnt robustly from about six learning examples. The
theory has been implemented as a computer model, which can bootstrap-learn a
language from zero vocabulary. The Bayesian learning mechanism is (1) Capable:
to learn arbitrarily complex meanings and syntactic structures; (2) Fast:
learning these structures from a few examples each; (3) Robust: learning in the
presence of much irrelevant noise, and (4) Self-repairing: able to acquire
implicit negative evidence, using it to learn exceptions. Children learning
language are clearly all of (1) - (4), whereas connectionist theories fail on
(1) and (2), and symbolic theories fail on (3) and (4). The theory is in good
agreement with many key facts of language acquisition, including facts which
are problematic for other theories. It is compared with over 100 key
cross-linguistic findings about acquisition of the lexicon, phrase structure,
morphology, complementation and control, auxiliaries, verb argument structures,
gaps and movement - in nearly all cases giving unforced agreement without extra
assumptions.
Related papers
- Language Generation: Complexity Barriers and Implications for Learning [51.449718747429756]
We show that even for simple and well-studied language families the number of examples required for successful generation can be extraordinarily large.<n>These results reveal a substantial gap between theoretical possibility and efficient learnability.
arXiv Detail & Related papers (2025-11-07T23:06:48Z) - A Unified Theory of Language [0.0]
A unified theory of language combines a Bayesian cognitive linguistic model of language processing.<n>The theory accounts for the major facts of language, including its speed and expressivity.<n>It proposes that language evolved by sexual selection for the display of intelligence.
arXiv Detail & Related papers (2025-08-14T11:09:15Z) - On the Thinking-Language Modeling Gap in Large Language Models [68.83670974539108]
We show that there is a significant gap between the modeling of languages and thoughts.<n>We propose a new prompt technique termed Language-of-Thoughts (LoT) to demonstrate and alleviate this gap.
arXiv Detail & Related papers (2025-05-19T09:31:52Z) - Hebbian learning the local structure of language [0.0]
We derive the foundations of an effective human language model inspired by microscopic constraints.
It has two parts: (1) a hierarchy of neurons which learns to tokenize words from text (whichiswhatyoudowhenyoureadthis); and (2) additional neurons which bind the learned symanticless patterns of the tokenizer into a symanticful token.
arXiv Detail & Related papers (2025-03-03T21:15:57Z) - A Complexity-Based Theory of Compositionality [53.025566128892066]
In AI, compositional representations can enable a powerful form of out-of-distribution generalization.
Here, we propose a formal definition of compositionality that accounts for and extends our intuitions about compositionality.
The definition is conceptually simple, quantitative, grounded in algorithmic information theory, and applicable to any representation.
arXiv Detail & Related papers (2024-10-18T18:37:27Z) - Reframing linguistic bootstrapping as joint inference using visually-grounded grammar induction models [31.006803764376475]
Semantic and syntactic bootstrapping posit that children use their prior knowledge of one linguistic domain, say syntactic relations, to help later acquire another, such as the meanings of new words.
Here, we argue that they are instead both contingent on a more general learning strategy for language acquisition: joint learning.
Using a series of neural visually-grounded grammar induction models, we demonstrate that both syntactic and semantic bootstrapping effects are strongest when syntax and semantics are learnt simultaneously.
arXiv Detail & Related papers (2024-06-17T18:01:06Z) - Universal Syntactic Structures: Modeling Syntax for Various Natural
Languages [0.0]
We aim to provide an explanation for how the human brain might connect words for sentence formation.
A novel approach to modeling syntactic representation is introduced, potentially showing the existence of universal syntactic structures for all natural languages.
arXiv Detail & Related papers (2023-12-28T20:44:26Z) - Learning the meanings of function words from grounded language using a visual question answering model [28.10687343493772]
We show that recent neural-network based visual question answering models can learn to use function words as part of answering questions about complex visual scenes.
We find that these models can learn the meanings of logical connectives and and or without any prior knowledge of logical reasoning.
Our findings offer proof-of-concept evidence that it is possible to learn the nuanced interpretations of function words in visually grounded context.
arXiv Detail & Related papers (2023-08-16T18:53:39Z) - From Word Models to World Models: Translating from Natural Language to
the Probabilistic Language of Thought [124.40905824051079]
We propose rational meaning construction, a computational framework for language-informed thinking.
We frame linguistic meaning as a context-sensitive mapping from natural language into a probabilistic language of thought.
We show that LLMs can generate context-sensitive translations that capture pragmatically-appropriate linguistic meanings.
We extend our framework to integrate cognitively-motivated symbolic modules.
arXiv Detail & Related papers (2023-06-22T05:14:00Z) - Verbal behavior without syntactic structures: beyond Skinner and Chomsky [0.0]
We must rediscover the extent to which language is like any other human behavior.
Recent psychological, computational, neurobiological, and evolutionary insights into the shaping and structure of behavior may point us toward a new, viable account of language.
arXiv Detail & Related papers (2023-03-11T00:01:21Z) - DALL-E 2 Fails to Reliably Capture Common Syntactic Processes [0.0]
We analyze the ability of DALL-E 2 to capture 8 grammatical phenomena pertaining to compositionality.
We show that DALL-E 2 is unable to reliably infer meanings that are consistent with the syntax.
arXiv Detail & Related papers (2022-10-23T23:56:54Z) - AUTOLEX: An Automatic Framework for Linguistic Exploration [93.89709486642666]
We propose an automatic framework that aims to ease linguists' discovery and extraction of concise descriptions of linguistic phenomena.
Specifically, we apply this framework to extract descriptions for three phenomena: morphological agreement, case marking, and word order.
We evaluate the descriptions with the help of language experts and propose a method for automated evaluation when human evaluation is infeasible.
arXiv Detail & Related papers (2022-03-25T20:37:30Z) - Neural Abstructions: Abstractions that Support Construction for Grounded
Language Learning [69.1137074774244]
Leveraging language interactions effectively requires addressing limitations in the two most common approaches to language grounding.
We introduce the idea of neural abstructions: a set of constraints on the inference procedure of a label-conditioned generative model.
We show that with this method a user population is able to build a semantic modification for an open-ended house task in Minecraft.
arXiv Detail & Related papers (2021-07-20T07:01:15Z) - Provable Limitations of Acquiring Meaning from Ungrounded Form: What
will Future Language Models Understand? [87.20342701232869]
We investigate the abilities of ungrounded systems to acquire meaning.
We study whether assertions enable a system to emulate representations preserving semantic relations like equivalence.
We find that assertions enable semantic emulation if all expressions in the language are referentially transparent.
However, if the language uses non-transparent patterns like variable binding, we show that emulation can become an uncomputable problem.
arXiv Detail & Related papers (2021-04-22T01:00:17Z) - Probing Pretrained Language Models for Lexical Semantics [76.73599166020307]
We present a systematic empirical analysis across six typologically diverse languages and five different lexical tasks.
Our results indicate patterns and best practices that hold universally, but also point to prominent variations across languages and tasks.
arXiv Detail & Related papers (2020-10-12T14:24:01Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.