Multi-turn Intent Determination for Goal-oriented Dialogue systems

Abstract

Intent determination is one of the main tasks of natural language understanding and dialogue systems, aiming to determine the intent of the user’s input. Incorporating contextual information for intent determination has shown much promise. Recently, memory networks have been used to encode context from the dialogue history at each turn. However, these methods lack general mechanism for encoding intent patterns. In this paper, we investigate the incorporation of intent patterns extracted from regular expression for the multi-turn intent determination task. We propose a novel neural network model with two memories. The first memory encodes context from dialogue history at each turn whereas the second memory contains the information obtained from the regular expression patterns. We evaluate the model on Frames and Key-Value Retrieval datasets, the experimental results demonstrate that encoding intent patterns with memory networks significantly improves the multi-turn intent determination.