Datasets:
File size: 4,522 Bytes
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license: cc-by-4.0
language:
- tr
pretty_name: TurBLiMP Turkish BLIMP
size_categories:
- 10K<n<100K
configs:
- config_name: anaphor_agreement
data_files: "data/anaphor_agreement*.parquet"
- config_name: argument_structure_transitive
data_files: "data/argument_structure_transitive*.parquet"
- config_name: argument_structure_ditransitive
data_files: "data/argument_structure_ditransitive*.parquet"
- config_name: binding
data_files: "data/binding*.parquet"
- config_name: determiners
data_files: "data/determiners*.parquet"
- config_name: ellipsis
data_files: "data/ellipsis*.parquet"
- config_name: irregular_forms
data_files: "data/irregular_forms*.parquet"
- config_name: island_effects
data_files: "data/island_effects*.parquet"
- config_name: nominalization
data_files: "data/nominalization*.parquet"
- config_name: npi_licensing
data_files: "data/npi_licensing*.parquet"
- config_name: passives
data_files: "data/passives*.parquet"
- config_name: quantifiers
data_files: "data/quantifiers*.parquet"
- config_name: relative_clauses
data_files: "data/relative_clauses*.parquet"
- config_name: scrambling
data_files: "data/scrambling*.parquet"
- config_name: subject_agreement
data_files: "data/subject_agreement*.parquet"
- config_name: suspended_affixation
data_files: "data/suspended_affixation*.parquet"
---
# TurBLiMP: Turkish BLIMP
## Dataset Description
TurBLiMP is the first Turkish benchmark of linguistic minimal pairs, designed to evaluate the linguistic abilities of monolingual and multilingual language models. The dataset covers 16 core grammatical phenomena in Turkish, with 1,000 minimal pairs per phenomenon.
## Dataset Structure
The dataset contains minimal pairs of grammatical and ungrammatical sentences in Turkish, organized into subsets testing various linguistic phenomena. Each minimal pair tests a specific grammatical phenomenon.
### Fields
- `sentence_good`: The grammatical sentence
- `sentence_bad`: The ungrammatical sentence
- `linguistic_phenomenon`: The linguistic phenomenon being tested
- `good_cue`: The cue word in the grammatical sentence
- `bad_cue`: The cue word in the ungrammatical sentence
- `critical_region`: The critical region being tested
- `item_id`: Unique identifier for the item
### Phenomena
1. **Anaphor Agreement** - Reflexive pronoun agreement violations
2. **Argument Structure (Transitive)** - Case marking errors with transitive verbs
3. **Argument Structure (Ditransitive)** - Case marking errors with ditransitive verbs
4. **Binding** - Principle B violations in binding theory
5. **Determiners** - Obligatory use of the indefinite article
6. **Ellipsis** - Backward gapping with non-parallel word orders
7. **Irregular Forms** - Incorrect aorist allomorph usage
8. **Island Effects** - Wh-adjunct extraction from complex NPs
9. **Nominalization** - Incorrect nominalization suffix selection
10. **NPI Licensing** - Negative polarity items in non-negative contexts
11. **Passives** - Unlicensed use of by-phrases in impersonal passives
12. **Quantifiers** - Quantifier usage with bare nouns
13. **Relative Clauses** - Incorrect case marking in relative clauses
14. **Scrambling** - Illicit postverbal scrambling from embedded clauses
15. **Subject Agreement** - Person/number agreement violations
16. **Suspended Affixation** - Improper tense suffix suspension
## Usage
```python
from datasets import load_dataset
# Load specific subset
dataset = load_dataset("juletxara/turblimp", "anaphor_agreement")
# Or load all subsets
dataset = load_dataset("juletxara/turblimp")
# Example usage
for subset_name, subset_data in dataset.items():
print(f"Subset: {subset_name}")
for example in subset_data:
print(f"Good: {example['sentence_good']}")
print(f"Bad: {example['sentence_bad']}")
print(f"Phenomenon: {example['linguistic_phenomenon']}")
print()
```
## Data Source
This dataset is derived from TurBLiMP:
https://github.com/ezgibasar/TurBLiMP
## Citation
Please cite the original TurBLiMP paper when using this dataset:
```bibtex
@misc{başar2025turblimpturkishbenchmarklinguistic,
title={TurBLiMP: A Turkish Benchmark of Linguistic Minimal Pairs},
author={Ezgi Başar and Francesca Padovani and Jaap Jumelet and Arianna Bisazza},
year={2025},
eprint={2506.13487},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2506.13487}
}
```
## License
The dataset follows the original TurBLiMP project's CC BY 4.0 licensing terms.
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