NLP 2018 Highlights：2018自然语言处理技术亮点汇总

即将收假，感兴趣的同学可以收下这份由 Elvis Saravia 在Github上发布的71页的PDF《NLP highlights of 2018》，汇总了2018年学术界和工业界NLP最重要的事件和技术亮点，涵盖强化学习、情感分析、NLP迁移学习、通用NLP、相关数据集等，感兴趣的同学可以直接访问 Github链接，也可点击文末“阅读原文“查看，PDF可直接从github上下载：

https://github.com/omarsar/nlp_highlights

以下是Github节选和部分报告节选：

NLP 2018 Highlights

It has been a big year for the field of natural language processing (NLP) and for machine learning as a whole. There have been many trends and breaking stories, with state-of-the-art results and new interesting research directions emerging. We owe all of this progress to the brilliant researchers around the world and the millions of developers devoting their full time to improve tools that make it easier for everyone to learn and progress the field. We have witnessed a rise in transfer learning and other niche areas of research such as AI Ethics.

In this report, I have provided a summary of all the biggest NLP stories of the year (2018) coming from both academia and the industry. I hope this report serves as a guide for the researcher and developer that wishes to start learning about this field and also for the expert who wishes to brush up on some of the latest advancements. The selected topics are based purely on personal observation, so it is highly possible that I missed other important stories. In fact, the stories shared here are an extended compilation of the NLP Newsletter published weekly on the dair.ai publication. I have made an effort to categorize the stories in the best possible way so that the report could benefit as many readers as possible. Please note that the report is meant to be non-technical for the purpose of reaching as many diverse readers as possible. The stories are mainly categorized by the following key topics: AI ethics, research publications, trends, education, resources, industry, and much more.

PDF of "NLP 2018 Highlights"

PDF全文请点击阅读原文在github上获取：

https://github.com/omarsar/nlp_highlights