Training a spoken language understanding system, as the one in Alexa, typically requires a large human-annotated corpus of data. Manual annotations are expensive and time consuming. In Alexa Skill Kit (ASK) user experience with the skill greatly depends on the amount of data provided by skill developer. In this work, we present an automatic natural language generation system, capable of generating both human-like interactions and annotations by the means of paraphrasing. Our approach consists of machine translation (MT) inspired encoder-decoder deep recurrent neural network. We evaluate our model on the impact it has on ASK skill, intent, named entity classification accuracy and sentence level coverage, all of which demonstrate significant improvements for unseen skills on natural language understanding (NLU) models, trained on the data augmented with paraphrases.

中文翻译：

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用于释义生成的神经机器翻译

训练口语理解系统（如 Alexa 中的系统）通常需要大量人工标注的数据语料库。手动注释既昂贵又耗时。在 Alexa Skill Kit (ASK) 中，用户对技能的体验很大程度上取决于技能开发人员提供的数据量。在这项工作中，我们提出了一个自动自然语言生成系统，能够通过释义的方式生成类似人类的交互和注释。我们的方法包括受机器翻译 (MT) 启发的编码器-解码器深度循环神经网络。我们评估我们的模型对 ASK 技能、意图、命名实体分类准确性和句子级别覆盖率的影响，所有这些都证明了对自然语言理解 (NLU) 模型中看不见的技能的显着改进，