A Copy-Augmented Sequence-to-Sequence Architecture Gives Good Performance on Task-Oriented Dialogue

Understand

Task-oriented dialogue focuses on conversational agents that participate in user-initiated dialogues 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.