thebogko/bulgarian-grammar-mistakes

Dataset of Bulgarian Grammar Mistakes

Dataset Description

Dataset Summary

This is a dataset of sentences in Bulgarian with grammar mistakes created by automatically inducing errors in correct sentences.

Supported Tasks

• text2text-generation: The dataset can be used to train a model for grammar error correction, which consists in correction of grammatical errors. in a source sentence, resulting in a correct version.

Languages

• bg: Only Bulgarian is supported by this dataset.

Dataset Structure

Data Instances

Each instance contains an error_type, which can be one of four pre-defined classes. This error_type describes the error found in the sequence in erroeneous which has been corrected in the sequence under correct.

{
  'error_type': 'article_misuse',
  'erroeneous': 'Отдясно на централния е входа за свещеници и монаси.',
  'correct': 'Отдясно на централния е входът за свещеници и монаси.'
}

Data Fields

• error_type: a string sequence that can be one of:
  • article_misuse
  • pronoun_misuse
  • incorrect_verb_suffix_me
  • noun_adjective_disagreement
• erroeneous: a string sequence of the erroneous sentence in the error-correct pair
• correct: a string sequence of the correct sentence in the error-correct pair

Data Splits

No pre-defined split has been applied to the dataset so the developer has the freedom to choose one that suits the task.

Dataset Creation

Curation Rationale

Main motivations for the creating of this dataset:

• resources for error correction NLP systems in Bulgarian are scarce, so this dataset aims to enocourage the development and evaluation of more such systems,
• solutions within Bulgarian NLP are traditional machine learning methods, so a dataset like this aims to encourage the development of state-of-the-art models (e.g. deep learning approaches)

Source Data

The source data for this dataset has been collected from Bulgarian Wikipedia articles and the OSCAR dataset.

Initial Data Collection and Normalization

The data collection process was such:

1. Data collection:

• Articles from Bulgarian Wikipedia were collected using Wikipedia's API.
• Rows from OSCAR's Bulgarian datasets were added to the collection.

2. The source texts underwent POS tagging and sentence segmentation using the tool Bulgarian NLP pipeline in CLaRK System developed by BulTreeBank Team.
3. Only sentences that (1) have three or more words, and (2) contain a token tagged as a verb were kept, as many of the sentences were simply article titles, links and other textual data we weren't interested in.

Introducing Grammatical Errors

The dataset has been created by introduced grammatical errors in correct sentences. Errors of the pre-defined types were induced using Python scripts, taking into account the error's nature, the algorithm is outlined as such:

• Take a source sentence, which would be kept as the reference.
• Induce an error of the pre-defined types, if that is possible (some sentences do not contain necessary pre-requisites for certain error types e.g. a relative pronoun is necessary to introduce an error of that type)
• Pair up the correct and erroneous versions

The resulting erroneous sentences were created with four different types of changes, reflected in the error_type column. In the examples below the first sequence is the CORRECT form, and the resulting sequence is ERRONEOUS.

1. article_misuse: switching the full/short article form for nouns and adjectives in the masculine grammatical gender
   • Replacing the full article with the short one: 'Политикът сподели плановете за новата реформа.' -> 'Политика сподели плановете за новата реформа.'
   • Replacing the short article with the full one: 'Галена седна на стола.' -> 'Галена седна на столът.'
2. pronoun_misuse: switching pronouns with their alternative forms in terms of case, number or grammatical gender.
   • Interrogative pronouns ('въпросителни местоимения' - кой, коя, кое)
     • Replacing the nominative form with the accusative one: 'Кой си милиш че си ти?' -> 'Кого мислиш че си ти?'
     • Replacing the accusative form with the nominative one: 'За кого говориш?' -> 'За кой говориш?'
   • Relative pronouns ('относителни местоимения' - който, която, което)
     • Replacing the nominative case form with the accusative one: 'Който не работи, не трябва да яде.' -> 'Когото не работи, не трябва да яде.'
     • Replacing the accusative case form with the nominative one: 'Няма човек тук, на когото Димо не се е похвалил.' -> 'Няма човек тук, на който Димо не се е похвалил.'
     • Replacing with respect to the grammatical gender and count:
       • 'Неизменно ги приветстваше с реч дама, чийто съпруг се представяше за голям патриот.' –> 'Неизменно ги приветстваше с реч дама, чиято съпруг се представяше за голям патриот.'
       • 'Ще работиш с режисьор, чиито възгледи граничат с абсурдното.' -> 'Ще работиш с режисьор, чийто възгледи граничат с абсурдното.'
3. incorrect_verb_suffix_me: Appending the character 'е' to plural verbs in the first person that end with 'м'.
   • 'пишем' -> 'пишеме'
   • 'водим' -> 'водиме'
   • 'вадим' -> 'вадиме'
   • 'тресем' -> 'тресеме'
4. noun_adjective_disagreement: Switching adjectives with their alternative forms in terms of grammatical gender and number when found in pair with a noun.
   • Replacing adjectives in adjective-noun pairs with respect to their grammatical gender:
     • 'Ще ти е нужен голям монитор.' -> 'Ще ти е нужен голяма монитор.'
     • 'Ще ти е нужен голям монитор.' -> 'Ще ти е нужен голямо монитор.'
     • 'Това е най-красивата сграда, която съм виждал.' -> 'Това е най-красивото сграда, която съм виждал.'
     • 'Това е най-красивата сграда, която съм виждал.' -> 'Това е най-красивия сграда, която съм виждал.'
     • 'Едно пухкаво зайче се появи в градината.' -> 'Едно пухкава зайче се появи в градината.'
     • 'Едно пухкаво зайче се появи в градината.' -> 'Едно пухкав зайче се появи в градината.'
   • Replacing adjectives in adjective-noun pairs with respect to their number (singular/plural):
     • 'Мартине, виждал ли си ми кафявите очила?' -> 'Мартине, виждал ли си ми кафявия очила?'
     • 'Сложи си нещата на бялата маса!' -> 'Сложи си нещата на белите маса!'

Personal and Sensitive Information

The source of this dataset is open-source data collections (Wikipedia and OSCAR) and carry the same amount of risk of personal and/or sensitive information as they do.

Considerations for Using the Data

Social Impact of Dataset

A dataset like this can be beneficial for language learners and developers in the error correction community.

Discussion of Biases

The error_type classes are not distributed equally, as some errors are more common than others. It's important the developer utlising this dataset is aware of this as to not create error correction system/evaluations that are biased.

Other Known Limitations

There are many grammatical errors not covered by this dataset, as it's the first of its kind. Hopefully, it encourages people to create more datasets like this and models that utilise them.

Additional Information

Licensing Information

The license of the dataset is apache2.0.