A Copy-Augmented Sequence-to-Sequence Architecture Gives Good Performance on Task-Oriented Dialogue
Mihail Eric, Christopher D. Manning · 2017
Task-oriented dialogue focuses on conversational agents that participate in dialogues with user goals on domain-specific topics.In contrast to chatbots, which simply seek to sustain open-ended meaningful discourse, existing task-oriented agents usually explicitly model user intent and belief states.This paper examines bypassing such an explicit representation by depending on a latent neural embedding of state and learning selective attention to dialogue history together with copying to incorporate relevant prior context.We complement recent work by showing the effectiveness of simple sequence-to-sequence neural architectures with a copy mechanism.Our model outperforms more complex memory-augmented models by 7% in per-response generation and is on par with the current state-of-the-art on DSTC2, a real-world task-oriented dialogue dataset.