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.

中文翻译：

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用于基于知识的面向任务的对话系统的模板引导的混合指针网络

大多数现有的基于神经网络的面向任务的对话系统都遵循编码器-解码器范式，其中解码器纯粹依赖源文本来生成单词序列，通常存在不稳定和可读性差的问题。受传统基于模板的生成方法的启发，我们为基于知识的面向任务的对话系统提出了一种模板引导的混合指针网络，该网络从预先构建的特定领域对话存储库中检索几个潜在相关的答案作为指导答案，并将指导答案合并到编码和解码过程中。具体来说，我们设计了一个具有门控机制的内存指针网络模型，以充分利用检索到的答案与真实响应之间的语义相关性。我们在四个广泛使用的面向任务的数据集上评估我们的模型，包括一个模拟数据集和三个手动创建的数据集。实验结果表明，在不同的自动评估指标上，所提出的模型的性能明显优于最先进的方法。