Learning Representations of Wordforms With Recurrent Networks: Comment on Sibley, Kello, Plaut, & Elman (2008)

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.