Context-Aware Sequence-to-Sequence Models for Conversational Systems

Understand

This work proposes a novel approach based on sequence-to-sequence (seq2seq) models for context-aware conversational systems.

• Exist- ing seq2seq models have been shown to be good for generating natural responses in a data-driven conversational system.
• However, they still lack mechanisms to incorporate previous conversation turns.
• We investigate RNN-based methods that efficiently integrate previous turns as a context for generating responses.