Chatter bots are computer programs that can simulate a conversation through text chat. Current chatter bots perform well in artificial conversations consisting of pairs of utterance exchanges like a question-answer session where the context switches with every pair. But they perform poorly in longer conversations where the context is maintained across several utterance exchanges. Existing approaches to artificial conversation generation focus on linguistic and grammatical modeling to generate individual sentence-level utterances. We present a framework that enables longer and more meaningful conversations by combining concepts of content representation and conversation semantics. We also present a metric for evaluating the conversations based on Grice's maxims, that form the central idea in the theory of pragmatics.
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