Discovering Faithful `Wickelfeature' Representations in a Connectionist Network
COLORADO UNIV AT BOULDER DEPT OF COMPUTER SCIENCE
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A challenging problem for connectionist models is the representation of varying-length sequences e.g., the sequence of phonemes that compose a word. One representation that has been proposed involves encoding each sequence element with respect to its local context this is known as a Wickelfeature representation. Handcrafted Wickelfeature representations suffer from a number of limitations, as pointed out by Pinker and Prince 1988. However, these limitations can be avoided if the representation is constructed with a priori knowledge of the set of possible sequences. This paper proposes a specialized connectionist network architecture and learning algorithm for the discovery of faithful Wickelfeature representations -- ones that do not lose critical information about the sequence to be encoded. The architecture is applied to a simplified version of Rumelhart and MeClellands 1986 verb past-tense model.
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