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Learning Representations of Wordforms With Recurrent Networks: Comment on

Content Provider	Semantic Scholar
Author	Bowers, Jeffrey S. Davis, Colin J.
Copyright Year	2009
Abstract	Sibley et al. (2008) report a recurrent neural network model designed to learn wordform representations suitable for written and spoken word identification. The authors claim that their sequence encoder network overcomes a key limitation associated with models that code letters by position (e.g., CAT might be coded as C-in-position-1, A-in-position-2, T-in-position-3). The problem with coding letters by position (slot-coding) is that it is difficult to generalize knowledge across positions; for example, the overlap between CAT and TOMCAT is lost. Although we agree this is a critical problem with many slot-coding schemes, we question whether the sequence encoder model addresses this limitation, and we highlight another deficiency of the model. We conclude that alternative theories are more promising.
Starting Page	353
Ending Page	361
Page Count	9
File Format	PDF HTM / HTML
Alternate Webpage(s)	https://www.wikidata.org/entity/Q51927387
Alternate Webpage(s)	http://seis.bris.ac.uk/~psjxb/bowers.davis.2009.pdf
PubMed reference number	21585501v1
Alternate Webpage(s)	https://doi.org/10.1111/j.1551-6709.2009.01062.x
DOI	10.1111/j.1551-6709.2009.01062.x
Journal	Cognitive Science
Volume Number	33
Issue Number	7
Language	English
Access Restriction	Open
Subject Keyword	Addresses (publication format) Angular defect Apache Tomcat Artificial neural network Encoder Device Component Network model Recurrent neural network Theory
Content Type	Text
Resource Type	Article

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