The ability to objectively quantify the complexity of a text can be a useful indicator of how likely learners of a given level will comprehend it. Before creating more complex models of assessing text difficulty, the basic building block of a text consists of words and, inherently, its overall difficulty is greatly influenced by the complexity of underlying words. One approach is to measure a word's Age of Acquisition (AoA), an estimate of the average age at which a speaker of a language understands the semantics of a specific word. Age of Exposure (AoE) statistically models the process of word learning, and in turn an estimate of a given word's AoA. In this paper, we expand on the model proposed by AoE by training regression models that learn and generalize AoA word lists across multiple languages including English, German, French, and Spanish. Our approach allows for the estimation of AoA scores for words that are not found in the original lists, up to the majority of the target language's vocabulary. Our method can be uniformly applied across multiple languages though the usage of parallel corpora and helps bridge the gap in the size of AoA word lists available for non-English languages. This effort is particularly important for efforts toward extending AI to languages with fewer resources and benchmarked corpora. [This paper was published in: "AIED 2021," edited by I. Roll et al., Springer Nature Switzerland AG, 2021, pp. 77-87.]