论文标题
混合监督对话系统的增强模型
Hybrid Supervised Reinforced Model for Dialogue Systems
论文作者
论文摘要
本文提出了基于深层Q-NETWORKS（DRQN）的以任务为导向对话系统的复发性混合模型和培训程序。该模型应对对话管理所需的两个任务：状态跟踪和决策。它基于将人机相互作用建模到嵌入相互作用上下文以指导讨论的潜在表示中。该模型的性能，学习速度和鲁棒性比非持续基线更高。此外，结果允许解释和验证策略演变和潜在表示信息。
This paper presents a recurrent hybrid model and training procedure for task-oriented dialogue systems based on Deep Recurrent Q-Networks (DRQN). The model copes with both tasks required for Dialogue Management: State Tracking and Decision Making. It is based on modeling Human-Machine interaction into a latent representation embedding an interaction context to guide the discussion. The model achieves greater performance, learning speed and robustness than a non-recurrent baseline. Moreover, results allow interpreting and validating the policy evolution and the latent representations information-wise.
