Conditional Generation and Snapshot Learning in Neural Dialogue Systems

Understand

Recently a variety of LSTM-based conditional language models (LM) have been applied across a range of language generation tasks.

• In this work we study various model architectures and different ways to represent and aggregate the source information in an end-to-end neural dialogue system framework.
• A method called snapshot learning is also proposed to facilitate learning from supervised sequential signals by applying a companion cross-entropy objective function to the conditioning vector.
• The experimental and analytical results demonstrate firstly that competition occurs between the conditioning vector and the LM, and the differing architectures provide different trade-offs between the two.