Over the recent number of years, neural networks have come to play an increasingly central role in natural language processing. Owing in large part to milestones such as word embeddings, and the explosion of chatbots powered by language models built, at least in part, by neural networks, the achievements of neural networks in the domain are come increasingly quickly. Trying to keep up with these advancements can be troublesome. That's where the today's spotlighted resource comes in. NLP Overview: Modern Deep Learning Techniques Applied to Natural Language Processing is a living resource maintained by Elvis Saravia and Soujanya Poria -- with a major part of the project having been directly borrowed from the work of Young et al. (2017), as per the resource maintainers.

This project contains an overview of recent trends in deep learning based natural language processing (NLP). It covers the theoretical descriptions and implementation details behind deep learning models, such as recurrent neural networks (RNNs), convolutional neural networks (CNNs), and reinforcement learning, used to solve various NLP tasks and applications. The overview also contains a summary of state of the art results for NLP tasks such as machine translation, question answering, and dialogue systems. There are various ways to contribute to this project. Refer to the issue section of the GitHub repository to learn more about how you can help. Or you can make suggestions by submitting a new issue.