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

• URL: http://arxiv.org/abs/2106.05830v1
• Date: Thu, 10 Jun 2021 15:49:26 GMT
• Title: A Template-guided Hybrid Pointer Network for Knowledge-basedTask-oriented Dialogue Systems
• Authors: Dingmin Wang, Ziyao Chen, Wanwei He, Li Zhong, Yunzhe Tao, Min Yang
• Abstract summary: We propose a template-guided hybrid pointer network for the knowledge-based task-oriented dialogue system. 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.
• Score: 15.654119998970499
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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