Evaluating Word Embeddings for Language Acquisition

Raquel G. Alhama, Caroline Rowland, Evan Kidd

Abstract
Continuous vector word representations (or word embeddings) have shown success in capturing semantic relations between words, as evidenced with evaluation against behavioral data of adult performance on semantic tasks (Pereira et al. 2016). Adult semantic knowledge is the endpoint of a language acquisition process; thus, a relevant question is whether these models can also capture emerging word representations of young language learners. However, the data of semantic knowledge of children is scarce or non-existent for some age groups. In this paper, we propose to bridge this gap by using Age of Acquisition norms to evaluate word embeddings learnt from child-directed input. We present two methods that evaluate word embeddings in terms of (a) the semantic neighbourhood density of learnt words, and (b) the convergence to adult word associations. We apply our methods to bag-of-words models, and we find that (1) children acquire words with fewer semantic neighbours earlier, and (2) young learners only attend to very local context. These findings provide converging evidence for validity of our methods in understanding the prerequisite features for a distributional model of word learning.
Anthology ID:
2020.cmcl-1.4
Volume:
Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics
Month:
November
Year:
2020
Address:
Online
Editors:
Emmanuele Chersoni, Cassandra Jacobs, Yohei Oseki, Laurent Prévot, Enrico Santus
Venue:
CMCL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
38–42
Language:
URL:
https://aclanthology.org/2020.cmcl-1.4
DOI:
10.18653/v1/2020.cmcl-1.4
Bibkey:
Cite (ACL):
Raquel G. Alhama, Caroline Rowland, and Evan Kidd. 2020. Evaluating Word Embeddings for Language Acquisition. In Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, pages 38–42, Online. Association for Computational Linguistics.
Cite (Informal):
Evaluating Word Embeddings for Language Acquisition (Alhama et al., CMCL 2020)
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