lefttribal.blogg.se - Deep learning for nlp

Refer to the issue section of the GitHub repository to learn more about how you can help. There are various ways to contribute to this project. Performance of Different Models on Different NLP Tasks.Unsupervised sentence representation learning.Reinforcement learning for sequence generation.Deep Reinforced Models and Deep Unsupervised Learning.

Parallelized Attention: The Transformer.

A collaborative project where expert researchers can suggest changes (e.g., incorporate SOTA results) based on their recent findings and experimental results.

Create a friendly and open resource to help guide researchers and anyone interested to learn about modern techniques applied to NLP.

Maintain an up-to-date learning resource that integrates important information related to NLP research, such as:.

The main motivations for this project are as follows:

The overview also contains a summary of state of the art results for NLP tasks such as machine translation, question answering, and dialogue systems. 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. This project contains an overview of recent trends in deep learning based natural language processing (NLP).