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We present a novel natural language generation system for spoken dialoguesystems capable of entraining (adapting) to users' way of speaking, providingcontextually appropriate responses. The generator is based on recurrent neuralnetworks and the sequence-to-sequence approach. It is fully trainable from datawhich include preceding context along with responses to be generated. We showthat the context-aware generator yields significant improvements over thebaseline in both automatic metrics and a human pairwise preference test.

A Context-aware Natural Language Generator for Dialogue Systems