From 1dc3c1e01c64a4200d046749d80d4bc321a4dec5 Mon Sep 17 00:00:00 2001 From: wehub-resource-sync Date: Mon, 13 Jul 2026 10:35:01 +0000 Subject: [PATCH] docs: preserve upstream English README --- README.en.md | 212 +++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 212 insertions(+) create mode 100644 README.en.md diff --git a/README.en.md b/README.en.md new file mode 100644 index 0000000..eac9ae9 --- /dev/null +++ b/README.en.md @@ -0,0 +1,212 @@ +# Tracking Progress in Natural Language Processing + +## Table of contents + +### English + +- [Automatic speech recognition](english/automatic_speech_recognition.md) +- [CCG](english/ccg.md) +- [Common sense](english/common_sense.md) +- [Constituency parsing](english/constituency_parsing.md) +- [Coreference resolution](english/coreference_resolution.md) +- [Data-to-Text Generation](english/data_to_text_generation.md) +- [Dependency parsing](english/dependency_parsing.md) +- [Dialogue](english/dialogue.md) +- [Domain adaptation](english/domain_adaptation.md) +- [Entity linking](english/entity_linking.md) +- [Grammatical error correction](english/grammatical_error_correction.md) +- [Information extraction](english/information_extraction.md) +- [Intent Detection and Slot Filling](english/intent_detection_slot_filling.md) +- [Keyphrase Extraction and Generation](english/keyphrase_extraction_generation.md) +- [Language modeling](english/language_modeling.md) +- [Lexical normalization](english/lexical_normalization.md) +- [Machine translation](english/machine_translation.md) +- [Missing elements](english/missing_elements.md) +- [Multi-task learning](english/multi-task_learning.md) +- [Multi-modal](english/multimodal.md) +- [Named entity recognition](english/named_entity_recognition.md) +- [Natural language inference](english/natural_language_inference.md) +- [Part-of-speech tagging](english/part-of-speech_tagging.md) +- [Paraphrase Generation](english/paraphrase-generation.md) +- [Question answering](english/question_answering.md) +- [Relation prediction](english/relation_prediction.md) +- [Relationship extraction](english/relationship_extraction.md) +- [Semantic textual similarity](english/semantic_textual_similarity.md) +- [Semantic parsing](english/semantic_parsing.md) +- [Semantic role labeling](english/semantic_role_labeling.md) +- [Sentiment analysis](english/sentiment_analysis.md) +- [Shallow syntax](english/shallow_syntax.md) +- [Simplification](english/simplification.md) +- [Stance detection](english/stance_detection.md) +- [Summarization](english/summarization.md) +- [Taxonomy learning](english/taxonomy_learning.md) +- [Temporal processing](english/temporal_processing.md) +- [Text classification](english/text_classification.md) +- [Word sense disambiguation](english/word_sense_disambiguation.md) + +### Vietnamese + +- [Dependency parsing](vietnamese/vietnamese.md#dependency-parsing) +- [Intent detection and Slot filling](vietnamese/vietnamese.md#intent-detection-and-slot-filling) +- [Machine translation](vietnamese/vietnamese.md#machine-translation) +- [Named entity recognition](vietnamese/vietnamese.md#named-entity-recognition) +- [Part-of-speech tagging](vietnamese/vietnamese.md#part-of-speech-tagging) +- [Semantic parsing](vietnamese/vietnamese.md#semantic-parsing) +- [Word segmentation](vietnamese/vietnamese.md#word-segmentation) + +### Hindi + +- [Chunking](hindi/hindi.md#chunking) +- [Part-of-speech tagging](hindi/hindi.md#part-of-speech-tagging) +- [Machine Translation](hindi/hindi.md#machine-translation) + +### Chinese + +- [Entity linking](chinese/chinese.md#entity-linking) +- [Chinese word segmentation](chinese/chinese_word_segmentation.md) +- [Question answering](chinese/question_answering.md) + +For more tasks, datasets and results in Chinese, check out the [Chinese NLP](https://chinesenlp.xyz/#/) website. + +### French + +- [Question answering](french/question_answering.md) +- [Summarization](french/summarization.md) + +### Russian + +- [Question answering](russian/question_answering.md) +- [Sentiment Analysis](russian/sentiment-analysis.md) +- [Summarization](russian/summarization.md) + +### Spanish + +- [Named Entity Recognition](spanish/named_entity_recognition.md) +- [Entity linking](spanish/entity_linking.md#entity-linking) +- [Summarization](spanish/summarization.md) + +### Portuguese + +- [Question Answering](portuguese/question_answering.md) + +### Korean + +- [Question Answering](korean/question_answering.md) + +### Nepali + +- [Machine Translation](nepali/nepali.md#machine-translation) + +### Bengali +- [Part-of-speech Tagging](bengali/part_of_speech_tagging.md) +- [Emotion Detection](bengali/emotion_detection.md) +- [Sentiment Analysis](bengali/sentiment_analysis.md) + +### Persian +- [Named entity recognition](persian/named_entity_recognition.md) +- [Natural language inference](persian/natural_language_inference.md) +- [Summarization](persian/summarization.md) + +### Turkish + +- [Summarization](turkish/summarization.md) + +### German + +- [Question Answering](german/question_answering.md) +- [Summarization](german/summarization.md) + +### Arabic +- [Language modeling](arabic/language_modeling.md) + + +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`](https://nlpprogress.com/) +or [`nlpsota.com`](http://nlpsota.com/) in your browser. + +### Contributing + +#### Guidelines + +**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 besides +the one that introduced the dataset. + +**Code**   We recommend to add a link to an implementation +if available. You can add a `Code` column (see below) to the table if it does not exist. +In the `Code` column, indicate an official implementation with [Official](http://link_to_implementation). +If an unofficial implementation is available, use [Link](http://link_to_implementation) (see below). +If no implementation is available, you can leave the cell empty. + +#### Adding a new result + +If you would like to add a new result, you can just click on the small edit button in the top-right +corner of the file for the respective task (see below). + +![Click on the edit button to add a file](img/edit_file.png) + +This allows you to edit the file in Markdown. Simply add a row to the corresponding table in the +same 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. + +![Fill out the file change information](img/propose_file_change.png) + +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". + +#### Adding a new dataset or task + +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/task + has multiple metrics, add them to the right of `Score`. +1. Submit your change as a pull request. + +| Model | Score | Paper / Source | Code | +| ------------- | :-----:| --- | --- | +| | | | | + + +### Wish list + +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) + +### Exporting into a structured format + +You can extract all the data into a structured, machine-readable JSON format with parsed tasks, descriptions and SOTA tables. + +The instructions are in [structured/README.md](structured/README.md). + +### Instructions for building the site locally + +Instructions for building the website locally using Jekyll can be found [here](jekyll_instructions.md). + +