论文标题
关于神经机器翻译的进化的深入演练
An In-depth Walkthrough on Evolution of Neural Machine Translation
论文作者
论文摘要
神经机器翻译（NMT）方法已经从使用简单的馈送架构到最新状态而蓬勃发展。即BERT模型。 NMT模型的用例已从仅仅语言翻译到对话代理（聊天机器人），抽象性文本摘要，图像字幕等等方面扩展，这些案例已被证明是其各自应用中的宝石。本文旨在研究神经机器翻译的主要趋势，域中最先进的模型以及它们之间的高水平比较。
Neural Machine Translation (NMT) methodologies have burgeoned from using simple feed-forward architectures to the state of the art; viz. BERT model. The use cases of NMT models have been broadened from just language translations to conversational agents (chatbots), abstractive text summarization, image captioning, etc. which have proved to be a gem in their respective applications. This paper aims to study the major trends in Neural Machine Translation, the state of the art models in the domain and a high level comparison between them.
