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This paper proposes collocational bootstrapping, a mechanism by which statistical word co-occurrence cues can aid the acquisition of English subject-verb agreement, supported by neural network simulations and analysis of child-directed speech.
This paper proposes LAD-inspired pre-pretraining using a formal language called MP-Struct that encodes natural-language-like structures. It shows that this approach improves token efficiency and imparts human-like resistance to structurally implausible languages, challenging prior hypotheses about effective pre-pretraining languages.
This paper presents a computational approach using large language models and RoBERTa to identify manner and result verbs in sentence context, achieving up to 89.6% accuracy. It aims to provide a scalable measurement tool for developmental language research.
This paper presents a computational framework to test competing maturational theories of syntactic development in children, specifically comparing bottom-up versus inward accounts using statistical grammar induction.