"nli_tr" 数据集简介

数据集概要

Natural Language Inference in Turkish (NLI-TR) 是一个由翻译基础 NLI 语料库（SNLI 和 MNLI）得到的一组大规模数据集，使用了 Amazon Translate 进行翻译。

支持的任务和排行榜

More Information Needed

语言

More Information Needed

数据集结构

数据实例

multinli_tr

• 下载的数据集文件大小： 75.52 MB
• 生成的数据集大小： 79.47 MB
• 总的磁盘使用量： 154.99 MB

'validation_matched' 的例子如下。

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

• 下载的数据集文件大小： 40.33 MB
• 生成的数据集大小： 73.89 MB
• 总的磁盘使用量： 114.22 MB

'train' 的例子如下。

{
    "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."
}

数据字段

所有拆分都具有相同的数据字段。

multinli_tr

• idx : 一个 int32 特征。
• premise : 一个 string 特征。
• hypothesis : 一个 string 特征。
• label : 一个分类标签，可能的值包括 entailment (0)， neutral (1)， contradiction (2)。

snli_tr

• idx : 一个 int32 特征。
• premise : 一个 string 特征。
• hypothesis : 一个 string 特征。
• label : 一个分类标签，可能的值包括 entailment (0)， neutral (1)， contradiction (2)。

数据拆分

multinli_tr

train	validation_matched	validation_mismatched
multinli_tr	392702	10000	10000

snli_tr

train	validation	test
snli_tr	550152	10000	10000

数据集创建

策划理由

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源数据

数据收集和标准化

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源语言制造者是谁？

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注释

注释过程

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注释者是谁？

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个人和敏感信息

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使用数据时的注意事项

数据的社会影响

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偏见讨论

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其他已知限制

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附加信息

数据集负责人

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许可信息

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引用信息

@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.",
}

贡献者

感谢 @e-budur 添加了这个数据集。