metadata

annotations_creators:
  - expert-generated
language:
  - ja
  - en
language_creators:
  - expert-generated
license:
  - cc-by-sa-4.0
multilinguality:
  - translation
pretty_name: JSICK
size_categories:
  - 1K<n<10K
source_datasets:
  - extended|sick
tags:
  - semantic-textual-similarity
  - sts
task_categories:
  - sentence-similarity
  - text-classification
task_ids:
  - natural-language-inference
  - semantic-similarity-scoring

Dataset Card for JaNLI

Dataset Card for JaNLI

Dataset Description

Homepage: https://github.com/verypluming/JSICK
Repository: https://github.com/verypluming/JSICK
Paper: https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00518/113850/Compositional-Evaluation-on-Japanese-Textual
Paper: https://www.jstage.jst.go.jp/article/pjsai/JSAI2021/0/JSAI2021_4J3GS6f02/_pdf/-char/ja

Dataset Summary

From official GitHub:

Japanese Sentences Involving Compositional Knowledge (JSICK) Dataset.

JSICK is the Japanese NLI and STS dataset by manually translating the English dataset SICK (Marelli et al., 2014) into Japanese. We hope that our dataset will be useful in research for realizing more advanced models that are capable of appropriately performing multilingual compositional inference.

JSICK-stress Test set

The JSICK-stress test set is a dataset to investigate whether models capture word order and case particles in Japanese. The JSICK-stress test set is provided by transforming syntactic structures of sentence pairs in JSICK, where we analyze whether models are attentive to word order and case particles to predict entailment labels and similarity scores.

The JSICK test set contains 1666, 797, and 1006 sentence pairs (A, B) whose premise sentences A (the column sentence_A_Ja_origin) include the basic word order involving ga-o (nominative-accusative), ga-ni (nominative-dative), and ga-de (nominative-instrumental/locative) relations, respectively.

We provide the JSICK-stress test set by transforming syntactic structures of these pairs by the following three ways:

scrum_ga_o: a scrambled pair, where the word order of premise sentences A is scrambled into o-ga, ni-ga, and de-ga order, respectively.
ex_ga_o: a rephrased pair, where the only case particles (ga, o, ni, de) in the premise A are swapped
del_ga_o: a rephrased pair, where the only case particles (ga, o, ni) in the premise A are deleted

Languages

The language data in JSICK is in Japanese and English.

Dataset Structure

Data Instances

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

import datasets as ds

dataset: ds.DatasetDict = ds.load_dataset("hpprc/jsick")
print(dataset)
# DatasetDict({
#     train: Dataset({
#         features: ['id', 'premise', 'hypothesis', 'label', 'score', 'premise_en', 'hypothesis_en', 'label_en', 'score_en', 'corr_entailment_labelAB_En', 'corr_entailment_labelBA_En', 'image_ID', 'original_caption', 'semtag_short', 'semtag_long'],
#         num_rows: 4500
#     })
#     test: Dataset({
#         features: ['id', 'premise', 'hypothesis', 'label', 'score', 'premise_en', 'hypothesis_en', 'label_en', 'score_en', 'corr_entailment_labelAB_En', 'corr_entailment_labelBA_En', 'image_ID', 'original_caption', 'semtag_short', 'semtag_long'],
#         num_rows: 4927
#     })
# })

dataset: ds.DatasetDict = ds.load_dataset("hpprc/jsick", name="stress")
print(dataset)
# DatasetDict({
#     test: Dataset({
#         features: ['id', 'premise', 'hypothesis', 'label', 'score', 'sentence_A_Ja_origin', 'entailment_label_origin', 'relatedness_score_Ja_origin', 'rephrase_type', 'case_particles'],
#         num_rows: 900
#     })
# })

base

An example of looks as follows:

{
    'id': 1,
    'premise': '子供たちのグループが庭で遊んでいて、後ろの方には年を取った男性が立っている',
    'hypothesis': '庭にいる男の子たちのグループが遊んでいて、男性が後ろの方に立っている',
    'label': 1, // (neutral)
    'score': 3.700000047683716,
    'premise_en': 'A group of kids is playing in a yard and an old man is standing in the background',
    'hypothesis_en': 'A group of boys in a yard is playing and a man is standing in the background',
    'label_en': 1, // (neutral)
    'score_en': 4.5,
    'corr_entailment_labelAB_En': 'nan',
    'corr_entailment_labelBA_En': 'nan',
    'image_ID': '3155657768_b83a7831e5.jpg',
    'original_caption': 'A group of children playing in a yard , a man in the background .',
    'semtag_short': 'nan',
    'semtag_long': 'nan',
}

stress

An example of looks as follows:

{
    'id': '5818_de_d',
    'premise': '女性火の近くダンスをしている',
    'hypothesis': '火の近くでダンスをしている女性は一人もいない',
    'label': 2,  // (contradiction)
    'score': 4.0,
    'sentence_A_Ja_origin': '女性が火の近くでダンスをしている',
    'entailment_label_origin': 2,
    'relatedness_score_Ja_origin': 3.700000047683716,
    'rephrase_type': 'd',
    'case_particles': 'de'
}

Data Fields

base

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

Name	Description
id	ids (the same with original SICK)
premise	first sentence in Japanese
hypothesis	second sentence in Japanese
label	entailment label in Japanese
score	relatedness score in the range [1-5] in Japanese
premise_en	first sentence in English
hypothesis_en	second sentence in English
label_en	original entailment label in English
score_en	original relatedness score in the range [1-5] in English
semtag_short	linguistic phenomena tags in Japanese
semtag_long	details of linguistic phenomena tags in Japanese
image_ID	original image in 8K ImageFlickr dataset
original_caption	original caption in 8K ImageFlickr dataset
corr_entailment_labelAB_En	corrected entailment label from A to B in English by (Karouli et al., 2017)
corr_entailment_labelBA_En	corrected entailment label from B to A in English by (Karouli et al., 2017)

stress

Name	Description
id	ids (the same with original SICK)
premise	first sentence in Japanese
hypothesis	second sentence in Japanese
label	entailment label in Japanese
score	relatedness score in the range [1-5] in Japanese
sentence_A_Ja_origin	the original premise sentences A from the JSICK test set.
entailment_label_origin	the original entailment labels
relatedness_score_Ja_origin	the original relatedness scores
rephrase_type	the type of transformation applied to the syntactic structures of the sentence pairs
case_particles	the grammatical particles in Japanese that indicate the function or role of a noun in a sentence.

Data Splits

name	train	test
base	4500	4927
original	4500	4927
stress		900
stress-original		900

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

Licensing Information

CC BY-SA 4.0

Citation Information

@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 and Koji Mineshima for creating this dataset.

Datasets:

hpprc
/

jsick

Dataset Card for JaNLI

Table of Contents

Dataset Description

Dataset Summary

Japanese Sentences Involving Compositional Knowledge (JSICK) Dataset.

JSICK-stress Test set

Languages

Dataset Structure

Data Instances

base

stress

Data Fields

base

stress

Data Splits

Annotations

Additional Information

Licensing Information

Citation Information

Contributions