Multilingual Age of Exposure

Botarleanu, Robert-Mihai; Dascălu, Mihai; Watanabe, Micah; McNamara, Danielle S.; Crossley, Scott A.

doi:10.1007/978-3-030-78292-4_7

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…The scores generated by our model and their corresponding features may be compared with equivalents in other languages in order to determine which terms are acquired later on in some languages. For example, an initial study of using AoE 2.0 for modeling multiple non-English languages (Botarleanu et al, 2021) shows that the word exposure trajectories are able to reflect some aspects of human word acquisition among various languages. Thus, specialized curricula suitable for particular language speakers may be constructed using AoE 2.0.…”

Section: Conclusion and Future Research Directionsmentioning

confidence: 99%

Age of Exposure 2.0: Estimating word complexity using iterative models of word embeddings

et al. 2022

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Age of acquisition (AoA) is a measure of word complexity which refers to the age at which a word is typically learned. AoA measures have shown strong correlations with reading comprehension, lexical decision times, and writing quality. AoA scores based on both adult and child data have limitations that allow for error in measurement, and increase the cost and effort to produce. In this paper, we introduce Age of Exposure (AoE) version 2, a proxy for human exposure to new vocabulary terms that expands AoA word lists through training regressors to predict AoA scores. Word2vec word embeddings are trained on cumulatively increasing corpora of texts, word exposure trajectories are generated by aligning the word2vec vector spaces, and features of words are derived for modeling AoA scores. Our prediction models achieve low errors (from 13% with a corresponding R 2 of .35 up to 7% with an R 2 of .74), can be uniformly applied to different AoA word lists, and generalize to the entire vocabulary of a language. Our method benefits from using existing readability indices to define the order of texts in the corpora, while the performed analyses confirm that the generated AoA scores accurately predicted the difficulty of texts (R 2 of .84, surpassing related previous work). Further, we provide evidence of the internal reliability of our word trajectory features, demonstrate the effectiveness of the word trajectory features when contrasted with simple lexical features, and show that the exclusion of features that rely on external resources does not significantly impact performance.

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Section: Conclusion and Future Research Directionsmentioning

confidence: 99%

Age of Exposure 2.0: Estimating word complexity using iterative models of word embeddings

et al. 2022

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Multilingual Age of Exposure 2.0

Botarleanu,

Watanabe,

Dascalu

et al. 2023

Int J Artif Intell Educ

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Distributional Semantics: Meaning Through Culture and Interaction

Contreras Kallens,

Christiansen

2024

Topics in Cognitive Science

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Mastering how to convey meanings using language is perhaps the main challenge facing any language learner. However, satisfactory accounts of how this is achieved, and even of what it is for a linguistic item to have meaning, are hard to come by. Nick Chater was one of the pioneers involved in the early development of one of the most successful methodologies within the cognitive science of language for discovering meaning: distributional semantics. In this article, we review this approach and discuss its successes and shortcomings in capturing semantic phenomena. In particular, we discuss what we dub the “distributional paradox:” how can models that do not implement essential dimensions of human semantic processing, such as sensorimotor grounding, capture so many meaning‐related phenomena? We conclude by providing a preliminary answer, arguing that distributional models capture the statistical scaffolding of human language acquisition that allows for communication, which, in line with Nick Chater's more recent ideas, has been shaped by the features of human cognition on the timescale of cultural evolution.

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Multilingual Age of Exposure

Cited by 3 publications

References 14 publications

Age of Exposure 2.0: Estimating word complexity using iterative models of word embeddings

Age of Exposure 2.0: Estimating word complexity using iterative models of word embeddings

Multilingual Age of Exposure 2.0

Distributional Semantics: Meaning Through Culture and Interaction

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