FPSeq: Simplifying and Accelerating Task-Oriented Dialogue Systems via Fully Parallel Sequence-to-Sequence Framework
Meina Song, Zhongfu Chen, Peiqing Niu, E Haihong · 2019
A mainstream task-oriented dialogue system is stuck in the independence of modules since it follows pipeline design, and it also suffers from sequence dependence and time dependence of recurrent neural network (RNN). Thus, these systems are complicated and usually trained slowly. In this paper, we propose FPSeq, a novel, fully parallel framework to simplify and accelerate task-oriented dialogue systems. Specifically, FPSeq turns pipeline design into a single sequence-to-sequence (seq2seq) model to achieve integration of each module for end-to-end training. In addition, multi-layer convolutional neural networks (CNNs) and attention mechanisms are applied in seq2seq learning to achieve parallel computations for speed improvement. Compared to the existing best model, the training speed of FPSeq is 3-10 times faster while only one-third of the number of parameters. Experimental results on CamRest676 and KVRET datasets indicate that FPSeq achieves the state-of-the-art performance in both task completion and quality of language generation.