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.
If you want to find this document again in the future, just go to nlpprogress.com
or nlpsota.com in your browser.
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Tracking Progress in Natural Language Processing
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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.
If you want to find this document again in the future, just go to
nlpprogress.com
ornlpsota.com
in your browser.