Grammatical Error Detection

The CoNLL dataset is a widely used resource in the field of natural language processing (NLP). The term “CoNLL” stands for Conference on Natural Language Learning. It originates from a series of shared tasks organized at the Conferences of Natural Language Learning.

The Cambridge Learner Corpus First Certificate in English (CLC FCE) dataset consists of short texts, written by learners of English as an additional language in response to exam prompts eliciting free-text answers and assessing mastery of the upper-intermediate proficiency level. The texts have been manually error-annotated using a taxonomy of 77 error types. The full dataset consists of 323,192 sentences. The publicly released subset of the dataset, named FCE-public, consists of 33,673 sentences split into test and training sets of 2,720 and 30,953 sentences, respectively. Source: Compositional Sequence Labeling Models for Error Detection in Learner Writing

JFLEG is for developing and evaluating grammatical error correction (GEC). Unlike other corpora, it represents a broad range of language proficiency levels and uses holistic fluency edits to not only correct grammatical errors but also make the original text more native sounding. Source: JFLEG: A Fluency Corpus and Benchmark for Grammatical Error Correction

Lang-8 Preprocessed Dataset (for GED): - Dataset: Lang-8, a publicly available dataset containing user-generated content, primarily from second-language learners, focused on writing errors. - Task: Grammatical Error Detection (GED). - Size: 200,000 sentences, with each sentence labeled as '0' for incorrect and '1' for its corrected version. - Preprocessing: The dataset has been cleaned and transformed to improve model performance, including removing noise, handling inconsistent annotations, and preparing it for training. - Usage: Ideal for training and evaluating models for GED and GEC.

• a fine-grained corpus to detect, identify and correct the chinese grammatical errors. * collected mainly from multi-choice questions in public school Chinese examinations * with multiple references * Online Evaluation Site for test set: https://codalab.lisn.upsaclay.fr/competitions/8020

We present a new annotated corpus of written learner English, derived from essays submitted to the learning platform Write & Improve (W&I). Users of W&I are presented with automated scoring and feedback on grammatical errors, and are encouraged to act on their error feedback, submitting multiple versions of their essays for any given prompt. We build the corpus on this interplay between users and prompts, collecting sets of essays submitted by users for a selected list of 50 popular prompts. The prompts include 20 aimed at beginner learners of English, 20 aimed at intermediate learners, and 10 at advanced learners. This distribution reflects the greater use of W&I by beginner and intermediate learners of English. We ensured that the prompts were not likely to elicit personal information and covered a broad range of tasks and topics. This list of prompts enabled us to identify 5050 essay sets written by 766 users, forming the basis for the Write & Improve Corpus, which is being made available for non-commercial use on the ELiT website. We describe the steps we took to ensure the corpus contains appropriate texts, does not include personal information, and will come with annotations relating to Common European Framework of Reference (CEFR) level and grammatical errors. All essays were submitted between 2020 and 2022 by registered users of W&I who have supplied their first language (L1) in an optional questionnaire. In total, there are more than 23K essays containing more than 3.5 million word tokens. The final versions of each essay set amount to 762K word tokens. There are 22 different L1s in the corpus, with the most common being Spanish, Portuguese, Japanese, Arabic and Vietnamese. We present some descriptive statistics for the corpus, and consider some research use cases.