Datasets:
license: cc-by-sa-4.0
task_categories:
- text-classification
language:
- cs
pretty_name: Czech SNLI
Dataset Card for Czech SNLI
Czech translation of the Stanford Natural Language Interface (SNLI) dataset with manual annotation of a SNLI subset. In addition to the entailment/contradiction/neutral inference, a "bad translation" class was added.
The annotation was done by students of NLP or computational linguistics. 1499 same pairs were annotated by two students to check IAA.
Dataset Details
The annotation for Czech premise-hypothesis pairs is done on 165390 pairs from train, test, and dev parts of the SNLI in the following distribution:
- train: 159650
- dev: 2860
- test: 2880
The premise-hypothesis pairs were translated using the LINDAT Translation at https://lindat.mff.cuni.cz/services/translation/. The CUBBITT model was published as:
Popel, M., Tomkova, M., Tomek, J. et al. Transforming machine translation: a deep learning system reaches news translation quality comparable to human professionals. Nat Commun 11, 4381 (2020). https://doi.org/10.1038/s41467-020-18073-9
Annotation
From the 165390 pairs, 151470 (91.58%) were considered understandable (i.e., they were not marked as "bad translation" but the translation may not be accurate enough to preserve the entailment).
Inter-Annotator Agreement
Two random annotators obtained the same dataset. The kappa score is 0.67 (substantial agreement).
Confusion matrix
Full report on the agreement
Simple Kappa Coefficient
--------------------------------
Kappa 0.6757
ASE 0.0146
95% Lower Conf Limit 0.6470
95% Upper Conf Limit 0.7044
Test of H0: Simple Kappa = 0
ASE under H0 0.0154
Z 43.9031
One-sided Pr > Z 0.0000
Two-sided Pr > |Z| 0.0000
Dataset Formats
Dataset is available as TSV and JSONL.
The JSONL version only contains pairs that were not annotated as "bad translation". In case of multiple annotations, only the agreed pairs (where both annotators agreed) are selected. The JSONL contains 149660 sentence pairs.