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Conversing with stochastic language models
In this paper we look at the problem of conversation simulation via stochastic language modeling. In particular, we show how a stochastic language model may be used to discover constraints which are beneficial when using the model generatively in the context of conversation simulation, we introduce a goal-oriented language model which is conductive to having its generations constrained in this way, and we discuss how the UpWrite, a technique borrowed from the field of syntactic pattern recognition, may be used to abstract the modelís representation of user input by discovering higher-level structure in the observed data.
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