Collocational bootstrapping: A hypothesis about the learning of subject-verb agreement in humans and neural networks

• URL: http://arxiv.org/abs/2605.20529v1
• Date: Tue, 19 May 2026 22:05:05 GMT
• Title: Collocational bootstrapping: A hypothesis about the learning of subject-verb agreement in humans and neural networks
• Abstract summary: We investigate whether regularities in word co-occurrence patterns can provide cues to syntactic dependencies.<n>We find that there is a range of variability levels at which statistical learners robustly learn subject-verb agreement.<n> Taken together, these results suggest that collocational bootstrapping is a viable learning strategy for the type of input that children receive.
• Score: 5.242425502046959
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In what ways might statistical signals in linguistic input assist with the acquisition of syntax? Here we hypothesize a mechanism called collocational bootstrapping, in which regularities in word co-occurrence patterns can provide cues to syntactic dependencies. We investigate whether this mechanism can support the acquisition of English subject-verb agreement. First, we simulate language acquisition by training neural networks on synthetic datasets that vary in how predictable their subject-verb pairings are. We find that there is a range of variability levels at which these statistical learners robustly learn subject-verb agreement. We then analyze the variability of subject-verb pairings in child-directed language, and we find that the variability in such data falls within the range that supported robust generalization in our computational simulations. Taken together, these results suggest that collocational bootstrapping is a viable learning strategy for the type of input that children receive.

Related papers

• Causally Evaluating the Learnability of Formal Language Tasks [59.73001113068827]
  We rigorously investigate the relationship between data frequency and learnability.<n>We derive Kullback-Leibler divergence metrics to measure the learnability of specific sub-tasks.<n>Our experiments show that evaluating learnability without causal intervention leads to incorrect conclusions.
  arXiv  Detail & Related papers  (2026-06-08T17:58:36Z)
• Deep networks learn to parse uniform-depth context-free languages from local statistics [12.183764229746926]
  Understanding how the structure of language can be learned from sentences alone is a central question in both cognitive science and machine learning.<n>We introduce a class of context-free grammars (PCFGs) in which both the degree of ambiguity and the correlation structure across scales can be controlled.<n>We propose a unifying framework where correlations at different scales lift local ambiguities, enabling the emergence of hierarchical representations of the data.
  arXiv  Detail & Related papers  (2026-01-31T17:35:06Z)
• Spatio-Temporal Graph Neural Networks for Infant Language Acquisition Prediction [0.0]
  A model of language acquisition for infants and young children can be constructed and adapted for use in a Spatio-Temporal Graph Convolutional Network (STGCN)<n>We introduce a novel approach for predicting child vocabulary acquisition, and evaluate the efficacy of such a model with respect to the different types of linguistic relationships that occur during language acquisition.
  arXiv  Detail & Related papers  (2025-03-18T15:21:27Z)
• The Emergence of Grammar through Reinforcement Learning [5.599852485003601]
  The evolution of grammatical systems of syntactic and semantic composition is modeled here with a novel application of reinforcement learning theory.<n>We include within the model a probability distribution over different messages that could be expressed in a given context.<n>The proposed learning and production algorithm then breaks down language learning into a sequence of simple steps, such as each step benefits from the message probabilities.
  arXiv  Detail & Related papers  (2025-03-03T15:10:46Z)
• Developmental Predictive Coding Model for Early Infancy Mono and Bilingual Vocal Continual Learning [69.8008228833895]
  We propose a small-sized generative neural network equipped with a continual learning mechanism.<n>Our model prioritizes interpretability and demonstrates the advantages of online learning.
  arXiv  Detail & Related papers  (2024-12-23T10:23:47Z)
• Learning Phonotactics from Linguistic Informants [54.086544221761486]
  Our model iteratively selects or synthesizes a data-point according to one of a range of information-theoretic policies. We find that the information-theoretic policies that our model uses to select items to query the informant achieve sample efficiency comparable to, or greater than, fully supervised approaches.
  arXiv  Detail & Related papers  (2024-05-08T00:18:56Z)
• A Latent-Variable Model for Intrinsic Probing [93.62808331764072]
  We propose a novel latent-variable formulation for constructing intrinsic probes.<n>We find empirical evidence that pre-trained representations develop a cross-lingually entangled notion of morphosyntax.
  arXiv  Detail & Related papers  (2022-01-20T15:01:12Z)
• Evaluating Models of Robust Word Recognition with Serial Reproduction [8.17947290421835]
  We compare several broad-coverage probabilistic generative language models in their ability to capture human linguistic expectations. We find that those models that make use of abstract representations of preceding linguistic context best predict the changes made by people in the course of serial reproduction.
  arXiv  Detail & Related papers  (2021-01-24T20:16:12Z)
• A Visuospatial Dataset for Naturalistic Verb Learning [18.654373173232205]
  We introduce a new dataset for training and evaluating grounded language models. Our data is collected within a virtual reality environment and is designed to emulate the quality of language data to which a pre-verbal child is likely to have access. We use the collected data to compare several distributional semantics models for verb learning.
  arXiv  Detail & Related papers  (2020-10-28T20:47:13Z)
• Modeling Voting for System Combination in Machine Translation [92.09572642019145]
  We propose an approach to modeling voting for system combination in machine translation. Our approach combines the advantages of statistical and neural methods since it can not only analyze the relations between hypotheses but also allow for end-to-end training.
  arXiv  Detail & Related papers  (2020-07-14T09:59:38Z)
• Mechanisms for Handling Nested Dependencies in Neural-Network Language Models and Humans [75.15855405318855]
  We studied whether a modern artificial neural network trained with "deep learning" methods mimics a central aspect of human sentence processing. Although the network was solely trained to predict the next word in a large corpus, analysis showed the emergence of specialized units that successfully handled local and long-distance syntactic agreement. We tested the model's predictions in a behavioral experiment where humans detected violations in number agreement in sentences with systematic variations in the singular/plural status of multiple nouns.
  arXiv  Detail & Related papers  (2020-06-19T12:00:05Z)

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.