This repository curated by Sebastian Ruder it’s a great source of techniques and benchmarks about the state-of-the-art in NLP research.
Several different methods can be found and most of the papers have the code of the implementation that can helps to reproduce the results and most important: The code can be forked and used in a customizable fashion for practical applications.
This document aims to track the progress in Natural Language Processing (NLP) and give an overview of the state-of-the-art (SOTA) across the most common NLP tasks and their corresponding datasets.
It aims to cover both traditional and core NLP tasks such as dependency parsing and part-of-speech tagging as well as more recent ones such as reading comprehension and natural language inference. The main objective is to provide the reader with a quick overview of benchmark datasets and the state-of-the-art for their task of interest, which serves as a stepping stone for further research. To this end, if there is a place where results for a task are already published and regularly maintained, such as a public leaderboard, the reader will be pointed there.