Abstract
It has been shown that the ability of echo state networks (ESNs) to generalise in a sentence-processing task can be increased by adjusting their input connection weights to the training data. We present a qualitative analysis of the effect of such weight adjustment on an ESN that is trained to perform the next-word prediction task. Our analysis makes use of CrySSMEx, an algorithm for extracting finite state machines (FSMs) from the data about the inputs, internal states, and outputs of recurrent neural networks that process symbol sequences. We find that the ESN with adjusted input weights yields a concise and comprehensible FSM. In contrast, the standard ESN, which shows poor generalisation, results in a massive and complex FSM. The extracted FSMs show how the two networks differ behaviourally. Moreover, poor generalisation is shown to correspond to a highly fragmented quantisation of the network's state space. Such findings indicate that CrySSMEx can be a useful tool for analysing ESN sentence processing.
Published Version
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