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spellcheck_benchmark

Tasks:
Text Generation
Languages:
Russian
ArXiv:
Tags:
spellcheck
russian
License:
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metadata
annotations_creators:
  - crowdsourced
language_creators:
  - crowdsourced
language:
  - ru
license:
  - apache-2.0
multilinguality:
  - monolingual
size_categories:
  - 1K<n<10k
task_categories:
  - text-generation
pretty_name: Russian Spellcheck Benchmark
language_bcp47:
  - ru-RU
tags:
  - spellcheck
  - russian

Dataset Card for Russian Spellcheck Benchmark

Table of Contents

Dataset Description

Dataset Summary

Spellcheck Benchmark includes four datasets, each of which consists of pairs of sentences in Russian language. Each pair embodies sentence, which may contain spelling errors, and its corresponding correction. Datasets were gathered from various sources and domains including social networks, internet blogs, github commits, medical anamnesis, literature, news, reviews and more.

All datasets were passed through two-stage manual labeling pipeline. The correction of a sentence is defined by an agreement of at least two human annotators. Manual labeling scheme accounts for jargonisms, collocations and common language, hence in some cases it encourages annotators not to amend a word in favor of preserving style of a text.

Supported Tasks and Leaderboards

Languages

Russian.

Dataset Structure

Data Instances

RUSpellRU

  • Size of downloaded dataset files: 3.64 Mb
  • Size of the generated dataset: 1.29 Mb
  • Total amount of disk used: 4.93 Mb

An example of "train" / "test" looks as follows

{
    "source": "очень классная тетка ктобы что не говорил.",
    "correction": "очень классная тетка кто бы что ни говорил",
}

MultidomainGold

  • Size of downloaded dataset files: 15.05 Mb
  • Size of the generated dataset: 5.43 Mb
  • Total amount of disk used: 20.48 Mb

An example of "test" looks as follows

{
    "source": "Ну что могу сказать... Я заказала 2 вязанных платья: за 1000 руб (у др продавца) и это ща 1200. Это платье- голимая синтетика (в том платье в составе была шерсть). Это платье как очень плохая резинка. На свои параметры (83-60-85) я заказала С . Пока одевала/снимала - оно в горловине растянулось. Помимо этого в этом платье я выгляжу ну очень тоской. У меня вес 43 кг на 165 см роста.  Кстати, продавец отправлял платье очень долго. Я пыталась отказаться от заказа, но он постоянно отклонял мой запрос. В общем не советую.",
    "correction": "Ну что могу сказать... Я заказала 2 вязаных платья: за 1000 руб (у др продавца) и это ща 1200. Это платье- голимая синтетика (в том платье в составе была шерсть). Это платье как очень плохая резинка. На свои параметры (83-60-85) я заказала С . Пока надевала/снимала - оно в горловине растянулось. Помимо этого в этом платье я выгляжу ну очень доской. У меня вес 43 кг на 165 см роста.  Кстати, продавец отправлял платье очень долго. Я пыталась отказаться от заказа, но он постоянно отклонял мой запрос. В общем не советую.",
    "domain": "reviews",
    
}

MedSpellcheck

  • Size of downloaded dataset files: 1.49 Mb
  • Size of the generated dataset: 0.54 Mb
  • Total amount of disk used: 2.03 Mb

An example of "test" looks as follows

{
    "source": "Кровотечения, поерации в анамнезе отрицает",
    "correction": "Кровотечения, операции в анамнезе отрицает",
}

GitHubTypoCorpusRu

  • Size of downloaded dataset files: 1.23 Mb
  • Size of the generated dataset: 0.48 Mb
  • Total amount of disk used: 1.71 Mb

An example of "test" looks as follows

{
    "source": "## Запросы и ответа содержат заголовки",
    "correction": "## Запросы и ответы содержат заголовки",
}

Data Fields

RUSpellRU

  • source: a string feature
  • correction: a string feature

MultidomainGold

  • source: a string feature
  • correction: a string feature
  • domain: a string feature

MedSpellcheck

  • source: a string feature
  • correction: a string feature

GitHubTypoCorpusRu

  • source: a string feature
  • correction: a string feature

Data Splits

RUSpellRU

train test
RUSpellRU 2000 2008

MultidomainGold

train test
web 386 756
news 361 245
social_media 430 200
reviews 584 586
subtitles 1810 1810
strategic_documents - 250
literature - 260

MedSpellcheck

test
MedSpellcheck 1054

GitHubTypoCorpusRu

test
GitHubTypoCorpusRu 868

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]

Annotations

Annotation process

We set up two-stage annotation project via a crowd-sourcing platform Toloka:

  1. Data gathering stage: we provide the texts with possible mistakes to annotators and ask them to write the sentence correctly;
  2. Validation stage: we provide annotators with the pair of sentences (source and its corresponding correction from the previous stage) and ask them to check if the correction is right.

Who are the annotators?

Native Russian speakers who passed the language exam.

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

The datasets we collected include large segments representing the Internet domain, and therefore, they may contain various stereotypes and biases. We acknowledge the potential for biases to emerge in the training data.

Other Known Limitations

A limitation of our study is the availability of different data for both the training and fine-tuning stages and the annotated data. The data used in our research may be limited to specific domains, preventing comprehensive coverage of all possible text variations.

Additional Information

Dataset Curators

Nikita Martynov nikita.martynov.98@list.ru

Licensing Information

All our datasets are published by MIT License.

Citation Information 


@inproceedings{martynov2023augmentation,
  title={Augmentation methods for spelling corruptions},
  author={Martynov, Nikita and Baushenko, Mark and Abramov, Alexander and Fenogenova, Alena},
  booktitle={Proceedings of the International Conference “Dialogue},
  volume={2023},
  year={2023}
}

@misc{martynov2023methodology,
      title={A Methodology for Generative Spelling Correction
via Natural Spelling Errors Emulation across Multiple Domains and Languages}, 
      author={Nikita Martynov and Mark Baushenko and Anastasia Kozlova and
Katerina Kolomeytseva and Aleksandr Abramov and Alena Fenogenova},
      year={2023},
      eprint={2308.09435},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}