We propose a novel methodology to address dialog learning in the context of goal-oriented conversational systems. The key idea is to quantize the dialog space into clusters and create a language model across the clusters, thus allowing for an accurate choice of the next utterance in the conversation. The language model relies on n-grams associated with clusters of utterances. This quantized-dialog language model methodology has been applied to the end-to-end goal-oriented track of the latest Dialog System Technology Challenges (DSTC6). The objective is to find the correct system utterance from a pool of candidates in order to complete a dialog between a user and an automated restaurant-reservation system. Our results show that the technique proposed in this paper achieves high accuracy regarding selection of the correct candidate utterance, and outperforms other state-of-the-art approaches based on neural networks.

使用基于簇的语言模型方法解决面向目标对话系统中的对话学习问题，在 DSTC6 挑战赛中应用于餐厅预订系统；结果表明，该技术在选择正确候选话语方面达到了高准确性，并且优于基于神经网络的其他现有方法。

面向目标导向对话系统的量化对话语言模型