Retrieval Enhanced Segment Generation Neural Network for Task-Oriented Dialogue Systems

Abstract

For task-oriented dialogue systems, Natural Language Generation (NLG) is the last and vital step which aims at generating an appropriate response according to the dialogue act (DA). While end-to-end neural networks have achieved promising performances on this task, the existing models still struggle to avoid slot mistakes. To address this challenge, we propose a novel segmented generation approach in this paper. The proposed method operates by progressively generating text for the span between two adjacent keywords (act type and slots) in semantically ordered DA. This procedure is recursively applied from left to right until a response is completed. Besides, a retrieval mechanism is utilized to better match the diversity and fluency in human language. Experimental results on four datasets demonstrate that our model achieves state-of-the-art slot error rate and also gets competitive performance on BLEU score with all strong baselines.