A Template-guided Hybrid Pointer Network for Knowledge-based Task-oriented Dialogue Systems

Dingmin Wang, Ziyao Chen, Wanwei He, Li Zhong, Yunzhe Tao, Min Yang


Abstract
Most existing neural network based task-oriented dialog 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 knowledge-based task-oriented dialog systems, 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.
Anthology ID:
2021.dialdoc-1.3
Volume:
Proceedings of the 1st Workshop on Document-grounded Dialogue and Conversational Question Answering (DialDoc 2021)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP | dialdoc
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
18–28
Language:
URL:
https://aclanthology.org/2021.dialdoc-1.3
DOI:
10.18653/v1/2021.dialdoc-1.3
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.dialdoc-1.3.pdf