CPC G06N 3/08 (2013.01) [G06F 40/232 (2020.01); G06F 40/284 (2020.01)] | 20 Claims |
1. A method of automatically scoring a constructed response using a neural network, comprising:
training the network from a set of examples with a set of mathematical operations to average network weights across training steps by exponential moving average or stochastic weight average;
receiving the constructed response at a processing system;
processing the constructed response with the processing system to divide the constructed response into multiple series of word tokens, wherein each word token includes a sequence of characters;
processing the constructed response with the processing system to correct one or more spelling errors by converting one or more of the word tokens into a canonical form;
encoding, with the processing system, the sequence of characters in each word token using character-level encoding to generate character representation vectors;
encoding, with the processing system, each word token using word-level encoding to generate word representation vectors;
concatenating the word and character representation vectors for each series of word tokens to generate a plurality of concatenated representation vectors for the constructed response;
applying a set of nonlinear operations to the plurality of concatenated representation vectors using a recurrent neural network to generate a single vector output, wherein the set of nonlinear operations comprise:
zt=σ(W(z)xt+U(z)ht−1+b(z));
rt=σ(W(r)xt+U(r)ht−1+b(r));
ht=tanh(W(z)xt+U(z)ht−1+b(z)); and
ht=zt∘ht−1+(1−zt)∘ht,
wherein each time step t has an input xt and hidden state ht; W(z), W(r), W ∈ nH×nI; U(z), U(r), U ∈ nH×nH; n is a hyperparameter; and
applying a set of predetermined network weights to the vector output of the recurrent neural network to generate a scalar output for scoring the constructed response.
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