Repository files navigation

• Automatic speech recognition
• CCG
• Common sense
• Constituency parsing
• Coreference resolution
• Data-to-Text Generation
• Dependency parsing
• Dialogue
• Domain adaptation
• Entity linking
• Grammatical error correction
• Information extraction
• Intent Detection and Slot Filling
• Keyphrase Extraction and Generation
• Language modeling
• Lexical normalization
• Machine translation
• Missing elements
• Multi-task learning
• Multi-modal
• Named entity recognition
• Natural language inference
• Part-of-speech tagging
• Paraphrase Generation
• Question answering
• Relation prediction
• Relationship extraction
• Semantic textual similarity
• Semantic parsing
• Semantic role labeling
• Sentiment analysis
• Shallow syntax
• Simplification
• Stance detection
• Summarization
• Taxonomy learning
• Temporal processing
• Text classification
• Word sense disambiguation

• Dependency parsing
• Intent detection and Slot filling
• Machine translation
• Named entity recognition
• Part-of-speech tagging
• Semantic parsing
• Word segmentation

• Chunking
• Part-of-speech tagging
• Machine Translation

• Entity linking
• Chinese word segmentation
• Question answering

For more tasks, datasets and results in Chinese, check out theChinese NLP website.

• Question answering
• Summarization

• Question answering
• Sentiment Analysis
• Summarization

• Named Entity Recognition
• Entity linking
• Summarization

• Question Answering

• Question Answering

• Machine Translation

• Part-of-speech Tagging
• Emotion Detection
• Sentiment Analysis

• Named entity recognition
• Natural language inference
• Summarization

• Summarization

• Question Answering
• Summarization

• Language modeling

This document aims to track the progress in Natural Language Processing (NLP) and give an overviewof 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 taggingas well as more recent ones such as reading comprehension and natural language inference. The main objectiveis to provide the reader with a quick overview of benchmark datasets and the state-of-the-art for theirtask of interest, which serves as a stepping stone for further research. To this end, if there is aplace 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 tonlpprogress.comornlpsota.com in your browser.

Results   Results reported in published papers are preferred; an exception may be made for influential preprints.

Datasets   Datasets should have been used for evaluation in at least one published paper besidesthe one that introduced the dataset.

Code   We recommend to add a link to an implementationif available. You can add aCode column (see below) to the table if it does not exist.In theCode column, indicate an official implementation withOfficial.If an unofficial implementation is available, useLink (see below).If no implementation is available, you can leave the cell empty.

If you would like to add a new result, you can just click on the small edit button in the top-rightcorner of the file for the respective task (see below).

This allows you to edit the file in Markdown. Simply add a row to the corresponding table in thesame format. Make sure that the table stays sorted (with the best result on top).After you've made your change, make sure that the table still looks ok by clicking on the"Preview changes" tab at the top of the page. If everything looks good, go to the bottom of the page,where you see the below form.

Add a name for your proposed change, an optional description, indicate that you would like to"Create a new branch for this commit and start a pull request", and click on "Propose file change".

For adding a new dataset or task, you can also follow the steps above. Alternatively, you can fork the repository.In both cases, follow the steps below:

1. If your task is completely new, create a new file and link to it in the table of contents above.
2. If not, add your task or dataset to the respective section of the corresponding file (in alphabetical order).
3. Briefly describe the dataset/task and include relevant references.
4. Describe the evaluation setting and evaluation metric.
5. Show how an annotated example of the dataset/task looks like.
6. Add a download link if available.
7. Copy the below table and fill in at least two results (including the state-of-the-art)for your dataset/task (change Score to the metric of your dataset). If your dataset/taskhas multiple metrics, add them to the right ofScore.
8. Submit your change as a pull request.

These are tasks and datasets that are still missing:

• Bilingual dictionary induction
• Discourse parsing
• Keyphrase extraction
• Knowledge base population (KBP)
• More dialogue tasks
• Semi-supervised learning
• Frame-semantic parsing (FrameNet full-sentence analysis)

You can extract all the data into a structured, machine-readable JSON format with parsed tasks, descriptions and SOTA tables.

The instructions are instructured/README.md.

Instructions for building the website locally using Jekyll can be foundhere.