# D.E. Rumelhart, G.E. Hinton and R.J. Williams ```Handout to accompany Rumelhart and McClelland: On learning the past tense of english verbs.
1.
Structure of the model
As you can see from Figure 1, the model is a feedforward neural network consisting of an input and an
output layer. Note that the connections between the “phonological representation form” and the
“wickelfeature representation form” are hardwired, as are the connections between the “wickelfeature
representation of root form” and the “phonological representation of past tense”. Only the connections
between the “wickelfeatures of root form” and “wickelfeatures of past tense” are subject to changes (or
learning). Thus we will consider only these last connections as part of “the network”.
2.
Operation of the model
The inputs to the network are binary (0 or 1), and the outputs are “probabilistic”. This means that at
each sampling step, the probability that a neuron is active (aj=1) or inactive (aj=0) is a function of the
summed input (netj). The figure below shows the logistic function used to calculate the probabilities
with different values of the “temperature” parameter.
1.2
1
0.8
T=2
T=1
T=0.5
T=0.2
0.6
0.4
0.2
-5
-3
0
-1
-0.2
1
3
5
Logistic function with different values of T. When T becomes small, the logistic function approaches a
step-function (threshold function or heavy-side function) and the output behavior of the units becomes
more and more deterministic.
3.
Leaning
As described during lecture, each example of the training set (a pair of root and past tense
wickelfeature representations) is presented at the input of the network. The corresponding output is
calculated as a function of the connection weights and the logistic function. The produced output is
then compared to the desired output (i.e. the “correct” past tense for a give root form), using the
perceptron convergence rule (see Lecture 3).
One feature of such a network is that it can extract regularities from a set of input patterns. In a way,
the network performs statistics, it finds what’s common among patterns and what’s different.
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