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A Connectionist Model of Speech Act Prediction
Abstract
We developed a connectionist architecture that accounts for the systematicity in the sequentialOTderingof speech act categories. That is, to what extent can the category of speech act n+1 be successfully predicted given speech acts 1 through n? Three connectionist architectures were contrasted: Elman's recurrent network, a single- entry backpropagation network, and a double-entry backpropagation network. T h e recurrent networkfit the speech act sequences in naturalistic conversation better than the backpropagation networks. M o s t of the systematicity w a s captured by the network's use of 2 to 3 prior speech acts of context.
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