将这段英文文本翻译成中文。


We propose a new method for generating natural language explanations of deep neural networks that provides a higher level of faithfulness-to-model, accuracy and fluency than existing methods. Our method generates explanations using a bi-directional pointer network, which allows it to directly reference words in the input and output sequences as part of the explanation. We evaluate our method on several datasets and show that it outperforms existing methods by a significant margin in terms of both faithfulness-to-model and human preference.
我们提出了一种新的方法，用于生成深度神经网络的自然语言解释，相比现有方法，它提供了更高的模型保真度、准确性和流畅性。我们的方法使用双向指针网络生成解释，允许直接引用输入和输出序列中的单词作为解释的一部分。我们在多个数据集上评估了我们的方法，并展示了它在保真度和人类偏好方面表现出显著优势。