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Dataset Card for "nli_tr"

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

More Information Needed

Languages

More Information Needed

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: a int32 feature.
  • premise: a string feature.
  • hypothesis: a string feature.
  • label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).

snli_tr

  • idx: a int32 feature.
  • premise: a string feature.
  • hypothesis: a string feature.
  • label: a classification label, with possible values including entailment (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

More Information Needed

Source Data

Initial Data Collection and Normalization

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Who are the source language producers?

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Annotations

Annotation process

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Who are the annotators?

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

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Licensing Information

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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.

Update on GitHub
Papers with Code

Models trained or fine-tuned on nli_tr