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Age of Acquisition (AoA) scores approximate the age at which a language speaker fully understands a word’s semantic meaning and represent a quantitative measure of the relative difficulty of words in a language. AoA word lists exist across various languages, with English having the most complete lists that capture the largest percentage of the vocabulary. In contrast, other languages have smaller lists making large-scale analyses difficult. Given the usefulness of AoA scores, methods have been developed to leverage the use of Machine Learning models to estimate AoA scores automatically through Age of Exposure (AoE) scores for the entire vocabulary of a language. These generated AoE scores use simulated learning trajectories to evaluate properties similar to AoA. In this work, we propose a method that leverages the greater size of existing English AoA lists to improve the performance of AoE prediction …