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UA-GEC: Grammatical Error Correction and Fluency Corpus for the Ukrainian Language


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UA-GEC: Grammatical Error Correction and Fluency Corpus for the Ukrainian Language

This repository contains UA-GEC data and an accompanying Python library.

What's new

  • May 2023: Shared Task on Ukrainian GEC results published.
  • November 2022: Version 2.0 released, featuring more data and detailed annotations.
  • January 2021: Initial release.

See for detailed updates.


All corpus data and metadata stay under the ./data. It has two subfolders for gec-fluency and gec-only corpus versions

Both corpus versions contain two subfolders train and test splits with different data representations:

./data/{gec-fluency,gec-only}/{train,test}/annotated stores documents in the annotated format

./data/{gec-fluency,gec-only}/{train,test}/source and ./data/{gec-fluency,gec-only}/{train,test}/target store the original and the corrected versions of documents. Text files in these directories are plain text with no annotation markup. These files were produced from the annotated data and are, in some way, redundant. We keep them because this format is convenient in some use cases.

./data/{gec-fluency,gec-only}/test/*.m2 contains M2 files.


./data/metadata.csv stores per-document metadata. It's a CSV file with the following fields:

  • id (str): document identifier;
  • author_id (str): document author identifier;
  • is_native (int): 1 if the author is native-speaker, 0 otherwise;
  • region (str): the author's region of birth. A special value "Інше" is used both for authors who were born outside Ukraine and authors who preferred not to specify their region.
  • gender (str): could be "Жіноча" (female), "Чоловіча" (male), or "Інша" (other);
  • occupation (str): one of "Технічна", "Гуманітарна", "Природнича", "Інша";
  • submission_type (str): one of "essay", "translation", or "text_donation";
  • source_language (str): for submissions of the "translation" type, this field indicates the source language of the translated text. Possible values are "de", "en", "fr", "ru", and "pl";
  • annotator_id (int): ID of the annotator who corrected the document;
  • partition (str): one of "test" or "train";
  • is_sensitive (int): 1 if the document contains profanity or offensive language.

Annotation format

Annotated files are text files that use the following in-text annotation format: {error=>edit:::error_type=Tag}, where error and edit stand for a text item before and after correction respectively, and Tag denotes an error category and an error subcategory in case of Grammar- and Fluency-related errors.

Example of an annotated sentence:

    I {likes=>like:::error_type=G/Number} turtles.

Below you can see a list of error types presented in the corpus:

  • Spelling: spelling errors;
  • Punctuation: punctuation errors.

Grammar-related errors:

  • G/Case: incorrect usage of case of any notional part of speech;
  • G/Gender: incorrect usage of gender of any notional part of speech;
  • G/Number: incorrect usage of number of any notional part of speech;
  • G/Aspect: incorrect usage of verb aspect;
  • G/Tense: incorrect usage of verb tense;
  • G/VerbVoice: incorrect usage of verb voice;
  • G/PartVoice: incorrect usage of participle voice;
  • G/VerbAForm: incorrect usage of an analytical verb form;
  • G/Prep: incorrect preposition usage;
  • G/Participle: incorrect usage of participles;
  • G/UngrammaticalStructure: digression from syntactic norms;
  • G/Comparison: incorrect formation of comparison degrees of adjectives and adverbs;
  • G/Conjunction: incorrect usage of conjunctions;
  • G/Other: other grammatical errors.

Fluency-related errors:

  • F/Style: style errors;
  • F/Calque: word-for-word translation from other languages;
  • F/Collocation: unnatural collocations;
  • F/PoorFlow: unnatural sentence flow;
  • F/Repetition: repetition of words;
  • F/Other: other fluency errors.

An accompanying Python package, ua_gec, provides many tools for working with annotated texts. See its documentation for details.

Train-test split

We expect users of the corpus to train and tune their models on the train split only. Feel free to further split it into train-dev (or use cross-validation).

Please use the test split only for reporting scores of your final model. In particular, never optimize on the test set. Do not tune hyperparameters on it. Do not use it for model selection in any way.

Next section lists the per-split statistics.


UA-GEC contains:


Split Documents Sentences Tokens Authors Errors
train 1,706 31,038 457,017 752 38,213
test 166 2,697 43,601 76 7,858
TOTAL 1,872 33,735 500,618 828 46,071

See stats.gec-fluency.txt for detailed statistics.


Split Documents Sentences Tokens Authors Errors
train 1,706 31,046 457,004 752 30,049
test 166 2,704 43,605 76 6,169
TOTAL 1,872 33,750 500,609 828 36,218

See stats.gec-only.txt for detailed statistics.

Python library

Alternatively to operating on data files directly, you may use a Python package called ua_gec. This package includes the data and has classes to iterate over documents, read metadata, work with annotations, etc.

Getting started

The package can be easily installed by pip:

    $ pip install ua_gec

Alternatively, you can install it from the source code:

    $ cd python
    $ python develop

Iterating through corpus

Once installed, you may get annotated documents from the Python code:

    >>> from ua_gec import Corpus
    >>> corpus = Corpus(partition="train", annotation_layer="gec-only")
    >>> for doc in corpus:
    ...     print(doc.source)         # "I likes it."
    ...     print(         # "I like it."
    ...     print(doc.annotated)      # <AnnotatedText("I {likes=>like} it.")
    ...     print(doc.meta.region)    # "Київська"

Note that the doc.annotated property is of type AnnotatedText. This class is described in the next section

Working with annotations

ua_gec.AnnotatedText is a class that provides tools for processing annotated texts. It can iterate over annotations, get annotation error type, remove some of the annotations, and more.

Here is an example to get you started. It will remove all F/Style annotations from a text:

    >>> from ua_gec import AnnotatedText
    >>> text = AnnotatedText("I {likes=>like:::error_type=G/Number} it.")
    >>> for ann in text.iter_annotations():
    ...     print(ann.source_text)       # likes
    ...     print(ann.top_suggestion)    # like
    ...     print(ann.meta)              # {'error_type': 'Grammar'}
    ...     if ann.meta["error_type"] == "F/Style":
    ...         text.remove(ann)         # or `text.apply_correction(ann)`

Multiple annotators

Some documents are annotated with multiple annotators. Such documents share doc_id but differ in doc.meta.annotator_id.

Currently, test sets for gec-fluency and gec-only are annotated by two annotators. The train sets contain 45 double-annotated docs.


  • Data and code improvements are welcomed. Please submit a pull request.


The accompanying paper is:

    title = "{UA}-{GEC}: Grammatical Error Correction and Fluency Corpus for the {U}krainian Language",
    author = "Syvokon, Oleksiy  and
      Nahorna, Olena  and
      Kuchmiichuk, Pavlo  and
      Osidach, Nastasiia",
    booktitle = "Proceedings of the Second Ukrainian Natural Language Processing Workshop (UNLP)",
    month = may,
    year = "2023",
    address = "Dubrovnik, Croatia",
    publisher = "Association for Computational Linguistics",
    url = "",
    pages = "96--102",
    abstract = "We present a corpus professionally annotated for grammatical error correction (GEC) and fluency edits in the Ukrainian language. We have built two versions of the corpus {--} GEC+Fluency and GEC-only {--} to differentiate the corpus application. To the best of our knowledge, this is the first GEC corpus for the Ukrainian language. We collected texts with errors (33,735 sentences) from a diverse pool of contributors, including both native and non-native speakers. The data cover a wide variety of writing domains, from text chats and essays to formal writing. Professional proofreaders corrected and annotated the corpus for errors relating to fluency, grammar, punctuation, and spelling. This corpus can be used for developing and evaluating GEC systems in Ukrainian. More generally, it can be used for researching multilingual and low-resource NLP, morphologically rich languages, document-level GEC, and fluency correction. The corpus is publicly available at",