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ArtikelLearning Representations of Wordforms With Recurrent Networks: Comment on Sibley, Kello, Plaut, & Elman  
Oleh: Bowers, Jeffrey S. ; Davis, Colin J.
Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: Cognitive Science vol. 33 no. 7 (Sep. 2009), page 1183–1186.
Topik: Slot-coding; Connection-ism; Alignment problem; Word identification; Symbols; Position-invariance
Fulltext: 01. Learning Representations of Wordforms With Recurrent Networks - Comment on.pdf (41.54KB)
Isi artikelSibley et al. (2008) report a recurrent neural network model designed to learn word form 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.
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