---
license: apache-2.0
language:
- ru
tags:
- benchmark
task_ids:
  - acceptability-classification
pretty_name: RuBLiMP
size_categories:
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    dtype: string
  - name: source_word
    dtype: string
  - name: target_word
    dtype: string
  - name: level
    dtype: string
  - name: phenomenon
    dtype: string
  - name: PID
    dtype: string
  - name: subtype
    dtype: string
  - name: domain
    dtype: string
  - name: tree_length
    dtype: int64
  splits:
  - name: train
    num_bytes: 415262
    num_examples: 1000
  download_size: 157018
  dataset_size: 415262
- config_name: subj_predicate_agreement_number_attractor
  features:
  - name: id
    dtype: int64
  - name: source_sentence
    dtype: string
  - name: target_sentence
    dtype: string
  - name: source_word
    dtype: string
  - name: target_word
    dtype: string
  - name: level
    dtype: string
  - name: phenomenon
    dtype: string
  - name: PID
    dtype: string
  - name: subtype
    dtype: string
  - name: domain
    dtype: string
  - name: tree_length
    dtype: int64
  splits:
  - name: train
    num_bytes: 392791
    num_examples: 1000
  download_size: 148021
  dataset_size: 392791
- config_name: tense_marker
  features:
  - name: id
    dtype: int64
  - name: source_sentence
    dtype: string
  - name: target_sentence
    dtype: string
  - name: source_word
    dtype: string
  - name: target_word
    dtype: string
  - name: level
    dtype: string
  - name: phenomenon
    dtype: string
  - name: PID
    dtype: string
  - name: subtype
    dtype: string
  - name: domain
    dtype: string
  - name: tree_length
    dtype: int64
  splits:
  - name: train
    num_bytes: 341641
    num_examples: 1000
  download_size: 131296
  dataset_size: 341641
- config_name: transitive_verb
  features:
  - name: id
    dtype: int64
  - name: source_sentence
    dtype: string
  - name: target_sentence
    dtype: string
  - name: source_word
    dtype: string
  - name: target_word
    dtype: string
  - name: level
    dtype: string
  - name: phenomenon
    dtype: string
  - name: PID
    dtype: string
  - name: subtype
    dtype: string
  - name: domain
    dtype: string
  - name: tree_length
    dtype: int64
  splits:
  - name: train
    num_bytes: 354503
    num_examples: 1000
  download_size: 155338
  dataset_size: 354503
- config_name: transitive_verb_iobject
  features:
  - name: id
    dtype: int64
  - name: source_sentence
    dtype: string
  - name: target_sentence
    dtype: string
  - name: source_word
    dtype: string
  - name: target_word
    dtype: string
  - name: level
    dtype: string
  - name: phenomenon
    dtype: string
  - name: PID
    dtype: string
  - name: subtype
    dtype: string
  - name: domain
    dtype: string
  - name: tree_length
    dtype: int64
  splits:
  - name: train
    num_bytes: 377788
    num_examples: 1000
  download_size: 156429
  dataset_size: 377788
- config_name: transitive_verb_object
  features:
  - name: id
    dtype: int64
  - name: source_sentence
    dtype: string
  - name: target_sentence
    dtype: string
  - name: source_word
    dtype: string
  - name: target_word
    dtype: string
  - name: level
    dtype: string
  - name: phenomenon
    dtype: string
  - name: PID
    dtype: string
  - name: subtype
    dtype: string
  - name: domain
    dtype: string
  - name: tree_length
    dtype: int64
  splits:
  - name: train
    num_bytes: 413517
    num_examples: 1000
  download_size: 179735
  dataset_size: 413517
- config_name: transitive_verb_passive
  features:
  - name: id
    dtype: int64
  - name: source_sentence
    dtype: string
  - name: target_sentence
    dtype: string
  - name: source_word
    dtype: string
  - name: target_word
    dtype: string
  - name: level
    dtype: string
  - name: phenomenon
    dtype: string
  - name: PID
    dtype: string
  - name: subtype
    dtype: string
  - name: domain
    dtype: string
  - name: tree_length
    dtype: int64
  splits:
  - name: train
    num_bytes: 545597
    num_examples: 1000
  download_size: 239590
  dataset_size: 545597
- config_name: transitive_verb_subject
  features:
  - name: id
    dtype: int64
  - name: source_sentence
    dtype: string
  - name: target_sentence
    dtype: string
  - name: source_word
    dtype: string
  - name: target_word
    dtype: string
  - name: level
    dtype: string
  - name: phenomenon
    dtype: string
  - name: PID
    dtype: string
  - name: subtype
    dtype: string
  - name: domain
    dtype: string
  - name: tree_length
    dtype: int64
  splits:
  - name: train
    num_bytes: 353677
    num_examples: 1000
  download_size: 145941
  dataset_size: 353677
- config_name: verb_acc_object
  features:
  - name: id
    dtype: int64
  - name: source_sentence
    dtype: string
  - name: target_sentence
    dtype: string
  - name: source_word
    dtype: string
  - name: target_word
    dtype: string
  - name: level
    dtype: string
  - name: phenomenon
    dtype: string
  - name: PID
    dtype: string
  - name: subtype
    dtype: string
  - name: domain
    dtype: string
  - name: tree_length
    dtype: int64
  splits:
  - name: train
    num_bytes: 303382
    num_examples: 1000
  download_size: 134081
  dataset_size: 303382
- config_name: verb_gen_object
  features:
  - name: id
    dtype: int64
  - name: source_sentence
    dtype: string
  - name: target_sentence
    dtype: string
  - name: source_word
    dtype: string
  - name: target_word
    dtype: string
  - name: level
    dtype: string
  - name: phenomenon
    dtype: string
  - name: PID
    dtype: string
  - name: subtype
    dtype: string
  - name: domain
    dtype: string
  - name: tree_length
    dtype: int64
  splits:
  - name: train
    num_bytes: 298421
    num_examples: 1000
  download_size: 123727
  dataset_size: 298421
- config_name: verb_ins_object
  features:
  - name: id
    dtype: int64
  - name: source_sentence
    dtype: string
  - name: target_sentence
    dtype: string
  - name: source_word
    dtype: string
  - name: target_word
    dtype: string
  - name: level
    dtype: string
  - name: phenomenon
    dtype: string
  - name: PID
    dtype: string
  - name: subtype
    dtype: string
  - name: domain
    dtype: string
  - name: tree_length
    dtype: int64
  splits:
  - name: train
    num_bytes: 378758
    num_examples: 1000
  download_size: 167590
  dataset_size: 378758
configs:
- config_name: add_new_suffix
  data_files:
  - split: train
    path: add_new_suffix/train-*
- config_name: add_verb_prefix
  data_files:
  - split: train
    path: add_verb_prefix/train-*
- config_name: adposition_government
  data_files:
  - split: train
    path: adposition_government/train-*
- config_name: anaphor_agreement_gender
  data_files:
  - split: train
    path: anaphor_agreement_gender/train-*
- config_name: anaphor_agreement_number
  data_files:
  - split: train
    path: anaphor_agreement_number/train-*
- config_name: change_declension_ending
  data_files:
  - split: train
    path: change_declension_ending/train-*
- config_name: change_declension_ending_has_dep
  data_files:
  - split: train
    path: change_declension_ending_has_dep/train-*
- config_name: change_duration_aspect
  data_files:
  - split: train
    path: change_duration_aspect/train-*
- config_name: change_repetition_aspect
  data_files:
  - split: train
    path: change_repetition_aspect/train-*
- config_name: change_verb_conjugation
  data_files:
  - split: train
    path: change_verb_conjugation/train-*
- config_name: change_verb_prefixes_order
  data_files:
  - split: train
    path: change_verb_prefixes_order/train-*
- config_name: clause_subj_predicate_agreement_gender
  data_files:
  - split: train
    path: clause_subj_predicate_agreement_gender/train-*
- config_name: clause_subj_predicate_agreement_number
  data_files:
  - split: train
    path: clause_subj_predicate_agreement_number/train-*
- config_name: clause_subj_predicate_agreement_person
  data_files:
  - split: train
    path: clause_subj_predicate_agreement_person/train-*
- config_name: conj_verb_tense
  data_files:
  - split: train
    path: conj_verb_tense/train-*
- config_name: deontic_imperative_aspect
  data_files:
  - split: train
    path: deontic_imperative_aspect/train-*
- config_name: external_possessor
  data_files:
  - split: train
    path: external_possessor/train-*
- config_name: floating_quantifier_agreement_case
  data_files:
  - split: train
    path: floating_quantifier_agreement_case/train-*
- config_name: floating_quantifier_agreement_gender
  data_files:
  - split: train
    path: floating_quantifier_agreement_gender/train-*
- config_name: floating_quantifier_agreement_number
  data_files:
  - split: train
    path: floating_quantifier_agreement_number/train-*
- config_name: genitive_subj_predicate_agreement_gender
  data_files:
  - split: train
    path: genitive_subj_predicate_agreement_gender/train-*
- config_name: genitive_subj_predicate_agreement_number
  data_files:
  - split: train
    path: genitive_subj_predicate_agreement_number/train-*
- config_name: genitive_subj_predicate_agreement_person
  data_files:
  - split: train
    path: genitive_subj_predicate_agreement_person/train-*
- config_name: indefinite_pronoun_to_negative
  data_files:
  - split: train
    path: indefinite_pronoun_to_negative/train-*
- config_name: negative_concord
  data_files:
  - split: train
    path: negative_concord/train-*
- config_name: negative_pronoun_to_indefinite
  data_files:
  - split: train
    path: negative_pronoun_to_indefinite/train-*
- config_name: nominalization_case
  data_files:
  - split: train
    path: nominalization_case/train-*
- config_name: noun_subj_predicate_agreement_gender
  data_files:
  - split: train
    path: noun_subj_predicate_agreement_gender/train-*
- config_name: noun_subj_predicate_agreement_number
  data_files:
  - split: train
    path: noun_subj_predicate_agreement_number/train-*
- config_name: noun_subj_predicate_agreement_person
  data_files:
  - split: train
    path: noun_subj_predicate_agreement_person/train-*
- config_name: np_agreement_case
  data_files:
  - split: train
    path: np_agreement_case/train-*
- config_name: np_agreement_gender
  data_files:
  - split: train
    path: np_agreement_gender/train-*
- config_name: np_agreement_number
  data_files:
  - split: train
    path: np_agreement_number/train-*
- config_name: single_verb_tense
  data_files:
  - split: train
    path: single_verb_tense/train-*
- config_name: subj_predicate_agreement_gender_attractor
  data_files:
  - split: train
    path: subj_predicate_agreement_gender_attractor/train-*
- config_name: subj_predicate_agreement_number_attractor
  data_files:
  - split: train
    path: subj_predicate_agreement_number_attractor/train-*
- config_name: tense_marker
  data_files:
  - split: train
    path: tense_marker/train-*
- config_name: transitive_verb
  data_files:
  - split: train
    path: transitive_verb/train-*
- config_name: transitive_verb_iobject
  data_files:
  - split: train
    path: transitive_verb_iobject/train-*
- config_name: transitive_verb_object
  data_files:
  - split: train
    path: transitive_verb_object/train-*
- config_name: transitive_verb_passive
  data_files:
  - split: train
    path: transitive_verb_passive/train-*
- config_name: transitive_verb_subject
  data_files:
  - split: train
    path: transitive_verb_subject/train-*
- config_name: verb_acc_object
  data_files:
  - split: train
    path: verb_acc_object/train-*
- config_name: verb_gen_object
  data_files:
  - split: train
    path: verb_gen_object/train-*
- config_name: verb_ins_object
  data_files:
  - split: train
    path: verb_ins_object/train-*
---
# RuBLiMP


## Dataset Description

**RuBLiMP**, or **Ru**ssian **B**enchmark of **Li**nguistic **M**inimal **P**airs, is the first diverse and large-scale benchmark of minimal pairs in Russian.

RuBLiMP includes **45k** minimal pairs of sentences that differ in grammaticality and isolate morphological, syntactic, or semantic phenomena. In contrast to existing benchmarks of linguistic minimal pairs, RuBLiMP is created by applying linguistic perturbations to automatically annotated sentences from open text corpora and decontaminating test data, making the minimal pairs more diverse and natural.


## Dataset Creation

![rublimp](rublimp.jpg)

Example: *Vpervye kosmonavt spal v nevesomosti* "For the first time an astronaut slept in zero gravity".

The RuBLiMP's creation approach comprises four stages:

- **(a)** Extract sentences from publicly available corpora of Wikipedia texts, news articles, and books.
- **(b)** Annotate each extracted sentence in the Universal Dependencies scheme with a multidomain morphosyntactic parser for Russian.
- **(c)** Search the dependency trees for specific lexical units and linguistic structures and apply expert-written perturbation rules to create a pool of minimal pairs for a target paradigm.
- **(d)** Compute [Min-K\% Prob](https://swj0419.github.io/detect-pretrain.github.io/) for each grammatical sentence in the pool using a set of LMs. Select *t* (the threshold for the maximum Min-K\% Prob value), which allows to find an intersection of 1k minimal pairs between the LMs. The minimal pairs in the intersection contain grammatical sentences that are not detected as the LMs' pretraining examples.


## Dataset Structure

### Data Instances

Each instance in the dataset is pair of sentences, where a certain linguistic phenomenon is isolated:

```
{
    'id': 240907, 
    'source_sentence': 'Это еще вчера все поняли.', 
    'target_sentence': 'Это еще завтра все поняли.', 
    'source_word': 'вчера', 
    'target_word': 'завтра', 
    'level': 'Semantics', 
    'phenomenon': 'Tense', 
    'PID': 'tense_marker',
    'subtype': 'tense_marker_simple',
    'domain': 'librusec',
    'tree_length': 2
}
```

An example in English for illustration purposes:

```
{
    'id': 240907, 
    'source_sentence': 'Everyone understood this yesterday already.', 
    'target_sentence': 'Everyone understood this tomorrow already.', 
    'source_word': 'yesterday', 
    'target_word': 'tomorrow', 
    'level': 'Semantics', 
    'phenomenon': 'Tense', 
    'PID': 'tense_marker',
    'subtype': 'tense_marker_simple',
    'domain': 'librusec',
    'tree_length': 2
}
```

### Data Fields
`id`: minimal pair identificator from the corpus \
`source_sentence`: original grammatical sentence \
`target_sentence`: perturbed ungrammatical sentence \
`source_word`: token in the original, undergoing the perturbation \
`target_word`: token in the ungrammatical sentence after the perturbation \
`level`: language level, targeted by the minimal pair (Syntax, Semantics or Morphology) \
`phenomenon`: targeted phenomenon \
`PID`: name of the paradigm \
`subtype`: type of the paradigm, if applicable  \
`domain`: sentence source (librusec, wikipedia or wikinews) \
`tree_length`: length, or depth, of the sentence's syntax tree


## Phenomena

### Morphology

<details>
    <summary><b>Word Formation</b></summary>

- **Addition of Extra Morphemes: Uninterpretable Suffix Combinations** (`add_new_suffix`) \
     Adding a new suffix to the noun or adjective to create a non-existing word

- **Addition of Extra Morphemes: Verb Prefixes** (`add_verb_prefix`) \
    Adding a prefix to a verb to create a violation of prefix stacking rules.

- **Morpheme Permutation: Verb Prefixes** (`change_verb_prefixes_order`) \
    Changing the order of the verb's prefixes to create a violation of prefix stacking rules.

</details>

<details>
    <summary><b>Word Inflection</b></summary>

- **Replacement of Inflectional Affixes: Noun Declensions (Simple)** (`change_declension_ending`) \
    Changing the inflectional suffixes of a noun to the suffixes of another declension

- **Replacement of Inflectional Affixes: Declensions of Nouns With Agreeing Dependents** (`change_declension_ending_has_dep`) \
    Changing the inflectional suffixes of a noun to the suffixes of another declension in the presence of an agreeing noun modifier

- **Inflectional Affixes: Verbal Conjugation Swap** (`change_verb_conjugation`) \
    Replacing the verb’s inflection with inflection of the opposite conjugation


</details>


### Syntax 
<details>
    <summary><b>Government</b></summary>

- **Prepositional Government** (`adp_government_case`) \
    Changing the case of a noun, governed by a preposition

- **Verbal Government: Direct Object** (`verb_acc_object`) \
    Changing the case of a direct verb object

- **Verbal Government: Genitive Object** (`verb_gen_object`) \
    Changing the case of an indirect verb object in Genitive case

- **Verbal Government: Object in Instrumental Case** (`verb_ins_object`) \
    Changing the case of an indirect verb object in Instrumental case

- **Verbal Government: Nominalizations** (`nominalization_cas`) \
    Changing the case of a dependent of a nominalization 

</details>


<details>
    <summary><b>Subject-Predicate Agreement</b></summary>

- **Subject-Predicate Agreement (Number)** (`noun_subj_predicate_agreement_number`) \
    Changing the number of the predicate to be distinct from its subject's (or, sometimes, changing number of the subject to be distinct from its predicate's) 

- **Genitive Subject-Predicate Agreement (Number)** (`genitive_subj_predicate_agreement_number`) \
    Changing the number of the predicate to plural, when subject is genitive and the agreement must be the default singular neuter

- **Clausal Subject-Predicate Agreement (Number)** (`clause_subj_predicate_agreement_number`) \
    Changing the number of the predicate to plural, when subject is a clause and the agreement must be the default singular neuter 

- **Subject-Predicate Agreement in Presence of an Attractor (Number)** (`subj_predicate_agreement_number_attractor`) \
    Changing the number of the verb to that, which is different from the subject, but the same as subject's dependent, or the attractor   

- **Subject-Predicate Agreement (Gender)** (`noun_subj_predicate_agreement_gender`) \
    Changing the gender of the predicate to be distinct from its subject's (or, sometimes, changing number of the subject to be distinct from its predicate's) 

- **Genitive Subject-Predicate Agreement (Gender)** (`genitive_subj_predicate_agreement_gender`)
    Changing the gender of the predicate to feminine or masculine, when subject is genitive and the agreement must be the default singular neuter

- **Clausal Subject-Predicate Agreement (Gender)** (`clause_subj_predicate_agreement_gender`) \
    Changing the gender of the predicate to feminine or masculine, when subject is a clause and the agreement must be the default singular neuter

- **Subject-Predicate Agreement in Presence of an Attractor (Gender)** (`subj_predicate_agreement_gender_attractor`) \
    Changing the gender of the verb to that, which is different from the subject, but the same as subject's dependent, or the attractor

- **Subject-Predicate Agreement (Person)** (`noun_subj_predicate_agreement_person`) \
    Changing the person of the predicate to be distinct from its subject's

- **Genitive Subject-Predicate Agreement (Person)** (`genitive_subj_predicate_agreement_person`) \ 
    Changing the person of the predicate to first or second person, when subject is genitive and the agreement must be the default third person singular

- **Clausal Subject-Predicate Agreement (Person)** (`clause_subj_predicate_agreement_person`) \ 
    Changing the person of the predicate to first or second person, when subject is a clause and the agreement must be the default third person singular
 
</details>


<details>
    <summary><b>Anaphor Agreement</b></summary>

- **Anaphor Agreement (Number)** (`anaphor_agreement_number`) \
    Changing the number of the relative pronoun or of its head noun 

- **Anaphor Agreement (Gender)** (`anaphor_agreement_gender`) \
    Changing the gender of the relative pronoun     

</details>

<details>
    <summary><b>Noun Phrase Agreement</b></summary>

- **Noun Phrase Agreement (Number)** (`np_agreement_number`) \
    Changing the number of an agreeing adjective 

- **Noun Phrase Agreement (Gender)** (`np_agreement_gender`) \
    Changing the gender of an agreeing adjective

- **Noun Phrase Agreement (Case)** (`np_agreement_case`) \
    Changing the case of an agreeing adjective 

</details>

<details>
    <summary><b>Floating Quantifier Agreement</b></summary>

- **Floating Quantifier Agreement (Number)** (`floating_quantifier_agreement_number`) \
    Changing the number of the quantifier or of the controller

- **Floating Quantifier Agreement (Gender)** (`floating_quantifier_agreement_gender`) \
    Changing the gender of the quantifier or of the controller

- **Floating Quantifier Agreement (Case)** (`floating_quantifier_agreement_case`) \
    Changing the case of the quantifier or of the controller

</details>



<details>
    <summary><b>Reflexives</b></summary>

- **External Possessor** (`external_possessor`) \
    Change a noun phrase or a pronoun to a reflexive pronoun sebya ‘self’ in a *u*-phrase inside the existential *be*-possessive construction.

</details>

<details>
    <summary><b>Negation</b></summary>

- **Negative Concord** (`negative_concord`) \
    Shifting the negative particle *ne* from a negated verb to another word in the sentence to violate negative concord rules.

- **Replacement of a Negative Pronoun with an Indefinite One** (`negative_pronoun_to_indefinite`) \
    Replacing an negative pronoun in the construction without a negated verb to an indefinite pronoun

- **Replacement of an Indefinite Pronoun with a Negative One** (`indefinite_pronoun_to_negative`) \
    Replacing an indefinite pronoun in the construction with a negated verb to a negative pronoun

</details>


### Semantics

<details>
    <summary><b>Argument Structure</b></summary>

- **Transitivity** (`transitive_verb`) \
    Replacing a transitive verb with an intransitive one

- **Animate Subject of a Transitive Verb** (`transitive_verb_subject`) \
    Swapping the subject and the direct object of a transitive verb or replacing the subject with a random inanimate word

- **Animate Subject of a Passive Verb** (`transitive_verb_passive`) \
    Swapping the subject and the direct object of a transitive verb in a passive construction or replacing the subject with a random inanimate word

- **Animate Direct Object of a Transitive Verb** (`transitive_verb_object`) \ 
    Replacing the direct object with a random inanimate word

- **Animate Indirect Object of a Transitive Verb** (`transitive_verb_iobject`) \ 
    Swapping the subject and the indirect object of a transitive verb or replacing the indirect subject of a transitive verb with a random inanimate word


</details>


<details>
    <summary><b>Aspect</b></summary>

- **Incompatibility of the Perfective with the Semantics of Duration** (`change_duration_aspect`) \
Replacing an imperfective verb with a perfective one in contexts with semantics of duration

- **Impossibility of the Perfective in Repetitive Situations** (`change_repetition_aspect`) \
Replacing an imperfective verb with a perfective one in contexts with semantics of repetition

- **Impossibility of the Perfective Under Negated Strong Deontic Verbs** (`deontic_imperative_aspect`) \
Replacing an imperfective verb with a perfective one in contexts with a negated deontic verb

</details>

<details>
    <summary><b>Tense</b></summary>

- **Tense** (`single_verb_tense`) \
    Changing verb tense in the presence of a temporal adverbial

- **Tense (coordination)** (`conj_verb_tense`) \
    Changing the tense of a conjoined verb in the presence of a temporal adverbial

- **Tense Markers** (`tense_marker`) \
    Changing a temporal adverbial in a sentence with a tense-marked verb

</details>


## Additional Information

### Dataset Curators

[Ekaterina Taktasheva](https://github.com/evtaktasheva) ([@evtaktasheva](https://huggingface.co/evtaktasheva)), [Maxim Bazhukov](https://github.com/bamaxi) ([@bamaxi](https://huggingface.co/bamaxi)), [Kirill Koncha](https://github.com/kirillkoncha) ([@midwestcyr](https://huggingface.co/midwestcyr)), [Ekaterina Artemova](https://github.com/artemovae) ([@KatyaA](https://huggingface.co/KatyaA)), [Vladislav Mikhailov](https://github.com/vmkhlv) ([@vmkhlv](https://huggingface.co/vmkhlv))


### Licensing Information

Apache 2.0

### Citation Information

```
@misc{taktasheva2024rublimprussianbenchmarklinguistic,
      title={RuBLiMP: Russian Benchmark of Linguistic Minimal Pairs}, 
      author={Ekaterina Taktasheva and Maxim Bazhukov and Kirill Koncha and Alena Fenogenova and Ekaterina Artemova and Vladislav Mikhailov},
      year={2024},
      eprint={2406.19232},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2406.19232}, 
}
```