A Context-aware Natural Language Generator for Dialogue Systems
This work addresses the challenge of improving user interaction in dialogue systems by enabling adaptation to users' speaking styles, though it appears incremental as it builds on existing neural network approaches.
The paper tackled the problem of generating contextually appropriate responses in spoken dialogue systems by introducing a context-aware natural language generator based on recurrent neural networks and sequence-to-sequence models, resulting in significant improvements over a baseline in both automatic metrics and human pairwise preference tests.
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.