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Computational and Robotic Models of Early Language Development: A Review

Abstract : We review computational and robotics models of early language learning and development. We first explain why and how these models are used to understand better how children learn language. We argue that they provide concrete theories of language learning as a complex dynamic system, complementing traditional methods in psychology and linguistics. We review different modeling formalisms, grounded in techniques from machine learning and artificial intelligence such as Bayesian and neural network approaches. We then discuss their role in understanding several key mechanisms of language development: cross-situational statistical learning, embodiment, situated social interaction, intrinsically motivated learning, and cultural evolution. We conclude by discussing future challenges for research, including modeling of large-scale empirical data about language acquisition in real-world environments.
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https://hal.inria.fr/hal-02371233
Contributor : Pierre-Yves Oudeyer Connect in order to contact the contributor
Submitted on : Tuesday, November 19, 2019 - 6:08:30 PM
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Pierre-Yves Oudeyer, George Kachergis, William Schueller. Computational and Robotic Models of Early Language Development: A Review. International Handbook of Language Acquisition, 2019. ⟨hal-02371233⟩

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