Datasets:
metadata
annotations_creators:
- expert-generated
language_creators:
- machine-generated
language:
- tr
license:
- cc-by-3.0
- cc-by-4.0
- cc-by-sa-3.0
- mit
- other
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended|snli
- extended|multi_nli
task_categories:
- text-classification
task_ids:
- natural-language-inference
- semantic-similarity-scoring
- text-scoring
paperswithcode_id: nli-tr
pretty_name: Natural Language Inference in Turkish
configs:
- multinli_tr
- snli_tr
license_details: Open Portion of the American National Corpus
dataset_info:
- config_name: snli_tr
features:
- name: idx
dtype: int32
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
splits:
- name: train
num_bytes: 71175743
num_examples: 550152
- name: validation
num_bytes: 1359639
num_examples: 10000
- name: test
num_bytes: 1355409
num_examples: 10000
download_size: 40328942
dataset_size: 73890791
- config_name: multinli_tr
features:
- name: idx
dtype: int32
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
splits:
- name: train
num_bytes: 75524150
num_examples: 392702
- name: validation_matched
num_bytes: 1908283
num_examples: 10000
- name: validation_mismatched
num_bytes: 2039392
num_examples: 10000
download_size: 75518512
dataset_size: 79471825
Dataset Card for "nli_tr"
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: https://github.com/boun-tabi/NLI-TR
- Repository: More Information Needed
- Paper: More Information Needed
- Point of Contact: More Information Needed
- Size of downloaded dataset files: 110.48 MB
- Size of the generated dataset: 146.26 MB
- Total amount of disk used: 256.74 MB
Dataset Summary
The Natural Language Inference in Turkish (NLI-TR) is a set of two large scale datasets that were obtained by translating the foundational NLI corpora (SNLI and MNLI) using Amazon Translate.
Supported Tasks and Leaderboards
Languages
Dataset Structure
Data Instances
multinli_tr
- Size of downloaded dataset files: 72.02 MB
- Size of the generated dataset: 75.79 MB
- Total amount of disk used: 147.81 MB
An example of 'validation_matched' looks as follows.
This example was too long and was cropped:
{
"hypothesis": "Mrinal Sen'in çalışmalarının çoğu Avrupa koleksiyonlarında bulunabilir.",
"idx": 7,
"label": 1,
"premise": "\"Kalküta, sanatsal yaratıcılığa dair herhangi bir iddiaya sahip olan tek diğer üretim merkezi gibi görünüyor, ama ironik bir şek..."
}
snli_tr
- Size of downloaded dataset files: 38.46 MB
- Size of the generated dataset: 70.47 MB
- Total amount of disk used: 108.93 MB
An example of 'train' looks as follows.
{
"hypothesis": "Yaşlı bir adam, kızının işten çıkmasını bekçiyken suyunu içer.",
"idx": 9,
"label": 1,
"premise": "Parlak renkli gömlek çalışanları arka planda gülümseme iken yaşlı bir adam bir kahve dükkanında küçük bir masada onun portakal suyu ile oturur."
}
Data Fields
The data fields are the same among all splits.
multinli_tr
idx: aint32feature.premise: astringfeature.hypothesis: astringfeature.label: a classification label, with possible values includingentailment(0),neutral(1),contradiction(2).
snli_tr
idx: aint32feature.premise: astringfeature.hypothesis: astringfeature.label: a classification label, with possible values includingentailment(0),neutral(1),contradiction(2).
Data Splits
multinli_tr
| train | validation_matched | validation_mismatched | |
|---|---|---|---|
| multinli_tr | 392702 | 10000 | 10000 |
snli_tr
| train | validation | test | |
|---|---|---|---|
| snli_tr | 550152 | 10000 | 10000 |
Dataset Creation
Curation Rationale
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
Personal and Sensitive Information
Considerations for Using the Data
Social Impact of Dataset
Discussion of Biases
Other Known Limitations
Additional Information
Dataset Curators
Licensing Information
Citation Information
@inproceedings{budur-etal-2020-data,
title = "Data and Representation for Turkish Natural Language Inference",
author = "Budur, Emrah and
"{O}zçelik, Rıza and
G"{u}ng"{o}r, Tunga",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
abstract = "Large annotated datasets in NLP are overwhelmingly in English. This is an obstacle to progress in other languages. Unfortunately, obtaining new annotated resources for each task in each language would be prohibitively expensive. At the same time, commercial machine translation systems are now robust. Can we leverage these systems to translate English-language datasets automatically? In this paper, we offer a positive response for natural language inference (NLI) in Turkish. We translated two large English NLI datasets into Turkish and had a team of experts validate their translation quality and fidelity to the original labels. Using these datasets, we address core issues of representation for Turkish NLI. We find that in-language embeddings are essential and that morphological parsing can be avoided where the training set is large. Finally, we show that models trained on our machine-translated datasets are successful on human-translated evaluation sets. We share all code, models, and data publicly.",
}
Contributions
Thanks to @e-budur for adding this dataset.