Datasets:

hpprc
/

janli

	---
	language:
	- ja
	language_creators:
	- other
	multilinguality:
	- monolingual
	pretty_name: JaNLI
	task_categories:
	- text-classification
	task_ids:
	- natural-language-inference
	license: cc-by-sa-4.0
	---

	# Dataset Card for JaNLI

	## Table of Contents
	- [Dataset Card for JaNLI](#dataset-card-for-janli)
	- [Table of Contents](#table-of-contents)
	- [Dataset Description](#dataset-description)
	- [Dataset Summary](#dataset-summary)
	- [Languages](#languages)
	- [Dataset Structure](#dataset-structure)
	- [Data Instances](#data-instances)
	- [base](#base)
	- [original](#original)
	- [Data Fields](#data-fields)
	- [base](#base-1)
	- [original](#original-1)
	- [Data Splits](#data-splits)
	- [Annotations](#annotations)
	- [Additional Information](#additional-information)
	- [Licensing Information](#licensing-information)
	- [Citation Information](#citation-information)
	- [Contributions](#contributions)

	## Dataset Description

	- Homepage: https://github.com/verypluming/JaNLI
	- Repository: https://github.com/verypluming/JaNLI
	- Paper: https://aclanthology.org/2021.blackboxnlp-1.26/

	### Dataset Summary

	The JaNLI (Japanese Adversarial NLI) dataset, inspired by the English HANS dataset, is designed to necessitate an understanding of Japanese linguistic phenomena and to illuminate the vulnerabilities of models.

	### Languages

	The language data in JaNLI is in Japanese (BCP-47 [ja-JP](https://www.rfc-editor.org/info/bcp47)).



	## Dataset Structure


	### Data Instances
	When loading a specific configuration, users has to append a version dependent suffix:

	```python
	import datasets as ds

	dataset: ds.DatasetDict = ds.load_dataset("hpprc/janli")
	print(dataset)
	# DatasetDict({
	# train: Dataset({
	# features: ['id', 'premise', 'hypothesis', 'label', 'heuristics', 'number_of_NPs', 'semtag'],
	# num_rows: 13680
	# })
	# test: Dataset({
	# features: ['id', 'premise', 'hypothesis', 'label', 'heuristics', 'number_of_NPs', 'semtag'],
	# num_rows: 720
	# })
	# })

	dataset: ds.DatasetDict = ds.load_dataset("hpprc/janli", name="original")
	print(dataset)
	# DatasetDict({
	# train: Dataset({
	# features: ['id', 'sentence_A_Ja', 'sentence_B_Ja', 'entailment_label_Ja', 'heuristics', 'number_of_NPs', 'semtag'],
	# num_rows: 13680
	# })
	# test: Dataset({
	# features: ['id', 'sentence_A_Ja', 'sentence_B_Ja', 'entailment_label_Ja', 'heuristics', 'number_of_NPs', 'semtag'],
	# num_rows: 720
	# })
	# })
	```


	#### base

	An example of looks as follows:

	```json
	{
	'id': 12,
	'premise': '若者がフットボール選手を見ている',
	'hypothesis': 'フットボール選手を若者が見ている',
	'label': 0,
	'heuristics': 'overlap-full',
	'number_of_NPs': 2,
	'semtag': 'scrambling'
	}
	```

	#### original

	An example of looks as follows:

	```json
	{
	'id': 12,
	'sentence_A_Ja': '若者がフットボール選手を見ている',
	'sentence_B_Ja': 'フットボール選手を若者が見ている',
	'entailment_label_Ja': 0,
	'heuristics': 'overlap-full',
	'number_of_NPs': 2,
	'semtag': 'scrambling'
	}
	```

	### Data Fields

	#### base

	A version adopting the column names of a typical NLI dataset.

	\| Name \| Description \|
	\| ------------- \| ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- \|
	\| id \| The number of the sentence pair. \|
	\| premise \| The premise (sentence_A_Ja). \|
	\| hypothesis \| The hypothesis (sentence_B_Ja). \|
	\| label \| The correct label for the sentence pair (either `entailment` or `non-entailment`); in the setting described in the paper, non-entailment = neutral + contradiction (entailment_label_Ja). \|
	\| heuristics \| The heuristics (structural pattern) tag. The tags are: subsequence, constituent, full-overlap, order-subset, and mixed-subset. \|
	\| number_of_NPs \| The number of noun phrase in a sentence. \|
	\| semtag \| The linguistic phenomena tag. \|


	#### original

	The original version retaining the unaltered column names.

	\| Name \| Description \|
	\| ------------------- \| ------------------------------------------------------------------------------------------------------------------------------------------------------------------- \|
	\| id \| The number of the sentence pair. \|
	\| sentence_A_Ja \| The premise. \|
	\| sentence_B_Ja \| The hypothesis. \|
	\| entailment_label_Ja \| The correct label for this sentence pair (either `entailment` or `non-entailment`); in the setting described in the paper, non-entailment = neutral + contradiction \|
	\| heuristics \| The heuristics (structural pattern) tag. The tags are: subsequence, constituent, full-overlap, order-subset, and mixed-subset. \|
	\| number_of_NPs \| The number of noun phrase in a sentence. \|
	\| semtag \| The linguistic phenomena tag. \|


	### Data Splits

	\| name \| train \| validation \| test \|
	\| -------- \| -----: \| ---------: \| ---: \|
	\| base \| 13,680 \| \| 720 \|
	\| original \| 13,680 \| \| 720 \|


	### Annotations

	The annotation process for this Japanese NLI dataset involves tagging each pair (P, H) of a premise and hypothesis with a label for structural pattern and linguistic phenomenon.
	The structural relationship between premise and hypothesis sentences is classified into five patterns, with each pattern associated with a type of heuristic that can lead to incorrect predictions of the entailment relation.
	Additionally, 11 categories of Japanese linguistic phenomena and constructions are focused on for generating the five patterns of adversarial inferences.

	For each linguistic phenomenon, a template for the premise sentence P is fixed, and multiple templates for hypothesis sentences H are created.
	In total, 144 templates for (P, H) pairs are produced.
	Each pair of premise and hypothesis sentences is tagged with an entailment label (`entailment` or `non-entailment`), a structural pattern, and a linguistic phenomenon label.

	The JaNLI dataset is generated by instantiating each template 100 times, resulting in a total of 14,400 examples.
	The same number of entailment and non-entailment examples are generated for each phenomenon.
	The structural patterns are annotated with the templates for each linguistic phenomenon, and the ratio of `entailment` and `non-entailment` examples is not necessarily 1:1 for each pattern.
	The dataset uses a total of 158 words (nouns and verbs), which occur more than 20 times in the JSICK and JSNLI datasets.


	## Additional Information

	- [verypluming/JaNLI](https://github.com/verypluming/JaNLI)
	- [Assessing the Generalization Capacity of Pre-trained Language Models through Japanese Adversarial Natural Language Inference](https://aclanthology.org/2021.blackboxnlp-1.26/)

	### Licensing Information

	CC BY-SA 4.0

	### Citation Information

	```bibtex
	@InProceedings{yanaka-EtAl:2021:blackbox,
	author = {Yanaka, Hitomi and Mineshima, Koji},
	title = {Assessing the Generalization Capacity of Pre-trained Language Models through Japanese Adversarial Natural Language Inference},
	booktitle = {Proceedings of the 2021 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP2021)},
	url = {https://aclanthology.org/2021.blackboxnlp-1.26/},
	year = {2021},
	}
	```

	### Contributions

	Thanks to [Hitomi Yanaka](https://hitomiyanaka.mystrikingly.com/) and [Koji Mineshima](https://abelard.flet.keio.ac.jp/person/minesima/index-j.html) for creating this dataset.