Revisiting Age of Acquisition in Curriculum Learning: Disentangling Lexical Features and Semantic Structure

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Natural Language Processing · Depth: Expert, quick

Summary

This study investigates whether ordering training data by children's Age of Acquisition (AoA) directly enhances distributional word embedding stability, or if correlated lexical features like word frequency, concreteness, and phonological complexity are the primary drivers. Researchers used incremental Word2Vec training to construct curricula ordered by AoA and individual lexical features, carefully controlling for vocabulary growth and deterministic ordering effects. Findings indicate that AoA-ordered curricula yield greater early-phase stability compared to shuffled baselines, even under controlled exposure conditions. While the observed AoA advantage is largely attributable to factors like overall word frequency, these embeddings demonstrate superior performance in predicting human AoA norms, suggesting they induce developmentally meaningful semantic structure despite limited gains on general similarity benchmarks.

Key takeaway

For NLP engineers developing specialized semantic models, consider integrating Age of Acquisition (AoA) curriculum learning, particularly when the task involves human cognitive development or language acquisition. While general similarity benchmarks show limited gains, AoA-ordered embeddings significantly improve performance on predicting human AoA norms. You should evaluate curriculum strategies against domain-specific proxy tasks to uncover benefits not apparent in broad evaluations, potentially leading to more robust and contextually relevant representations.

Key insights

Curriculum learning ordered by Age of Acquisition (AoA) induces developmentally meaningful semantic structure in word embeddings.

Principles

• AoA curricula enhance early embedding stability.
• Word frequency largely explains AoA benefits.
• AoA ordering improves human AoA norm prediction.

Method

Incremental Word2Vec training was used to construct curricula ordered by Age of Acquisition (AoA) and individual lexical features, controlling for vocabulary growth and ordering effects.

In practice

• Consider AoA for specialized semantic tasks.
• Evaluate curriculum effects beyond general benchmarks.

Topics

• Curriculum Learning
• Age of Acquisition
• Word Embeddings
• Word2Vec
• Lexical Semantics
• Distributional Semantics

Best for: AI Scientist, NLP Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.