Most existing neural network based task-oriented dialogue systems follow encoder-decoder paradigm, where the decoder purely depends on the source texts to generate a sequence of words, usually suffering from instability and poor readability. Inspired by the traditional template-based generation approaches, we propose a template-guided hybrid pointer network for the knowledge-based task-oriented dialogue system, which retrieves several potentially relevant answers from a pre-constructed domain-specific conversational repository as guidance answers, and incorporates the guidance answers into both the encoding and decoding processes. Specifically, we design a memory pointer network model with a gating mechanism to fully exploit the semantic correlation between the retrieved answers and the ground-truth response. We evaluate our model on four widely used task-oriented datasets, including one simulated and three manually created datasets. The experimental results demonstrate that the proposed model achieves significantly better performance than the state-of-the-art methods over different automatic evaluation metrics.
@article{arxiv.2106.05830,
title = {A Template-guided Hybrid Pointer Network for Knowledge-basedTask-oriented Dialogue Systems},
author = {Dingmin Wang and Ziyao Chen and Wanwei He and Li Zhong and Yunzhe Tao and Min Yang},
journal= {arXiv preprint arXiv:2106.05830},
year = {2021}
}