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  Evaluating word embeddings for language acquisition

Alhama, R. G., Rowland, C. F., & Kidd, E. (2020). Evaluating word embeddings for language acquisition. In E. Chersoni, C. Jacobs, Y. Oseki, L. Prévot, & E. Santus (Eds.), Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics (pp. 38-42). Stroudsburg, PA, USA: Association for Computational Linguistics (ACL).

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Item Permalink: https://hdl.handle.net/21.11116/0000-0007-7091-6 Version Permalink: https://hdl.handle.net/21.11116/0000-0007-7A4B-D
Genre: Conference Paper

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Alhama_Rowland_Kidd_2020_Evaluating word embeddings for language acquisition.pdf (Publisher version), 273KB
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Alhama_Rowland_Kidd_2020_Evaluating word embeddings for language acquisition.pdf
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 Creators:
Alhama, Raquel G.1, 2, Author           
Rowland, Caroline F.1, 3, Author           
Kidd, Evan1, 3, 4, 5, Author           
Affiliations:
1Language Development Department, MPI for Psycholinguistics, Max Planck Society, ou_2340691              
2Tilburg University, Tilburg, The Netherlands, ou_persistent22              
3Donders Institute for Brain, Cognition and Behaviour, External Organizations, ou_55236              
4Australian National University, Canberra, Australia, ou_persistent22              
5ARC Centre of Excellence for the Dynamics of Language, Canberra, Australia, ou_persistent22              

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 Abstract: Continuous vector word representations (or
word embeddings) have shown success in cap-turing semantic relations between words, as evidenced by evaluation against behavioral data of adult performance on semantic tasks (Pereira et al., 2016). Adult semantic knowl-edge 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 for children’s semantic knowledge across development is scarce. 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) con-
vergence to adult word associations. We apply our methods to bag-of-words models, and 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.

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Language(s): eng - English
 Dates: 2020-11-19
 Publication Status: Published online
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 Rev. Type: Peer
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Title: (Online) Workshop on Cognitive Modeling and Computational Linguistics (CMCL 2020)
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Start-/End Date: 2020-11-19

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Title: Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics
Source Genre: Proceedings
 Creator(s):
Chersoni, Emmanuele, Editor
Jacobs, Cassandra, Editor
Oseki, Yohei, Editor
Prévot, Laurent, Editor
Santus, Enrico, Editor
Affiliations:
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Publ. Info: Stroudsburg, PA, USA : Association for Computational Linguistics (ACL)
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 38 - 42 Identifier: -