A Context-aware Natural Language Generator for Dialogue Systems

Understand

We present a novel natural language generation system for spoken dialogue systems capable of entraining (adapting) to users' way of speaking, providing contextually appropriate responses.

• The generator is based on recurrent neural networks and the sequence-to-sequence approach.
• It is fully trainable from data which include preceding context along with responses to be generated.
• We show that the context-aware generator yields significant improvements over the baseline in both automatic metrics and a human pairwise preference test.