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

TL;DR

This paper introduces a template-guided hybrid pointer network that enhances knowledge-based task-oriented dialogue systems by retrieving relevant answers and integrating them into encoding and decoding, leading to improved performance.

Contribution

It proposes a novel memory pointer network with gating mechanism that leverages retrieved answers to improve dialogue generation in knowledge-based systems.

Findings

Achieves significantly better performance than state-of-the-art methods.

Effective in utilizing retrieved answers for improved response quality.

Validated on four diverse task-oriented datasets.

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.