论文标题
术产生的神经机器翻译
Neural Machine Translation For Paraphrase Generation
论文作者
论文摘要
培训一种语言理解系统，就像Alexa中的语言系统，通常需要大量的人类通知的数据语料库。手动注释昂贵且耗时。在Alexa Skill套件（询问）用户体验中，具有该技能的用户体验在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，我们提出了一种自动自然语言生成系统，能够通过释义来产生类似人类的相互作用和注释。我们的方法由机器翻译（MT）启发的编码器折射深度复发神经网络组成。我们评估了我们对询问技能，意图，命名实体分类准确性和句子级别覆盖的影响的模型，所有这些都表现出对自然语言理解（NLU）模型的显着提高，并在对数据增强的数据中进行了培训。
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.
