README.md

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

Table of Contents

• Dataset Card for JaNLI
  • Table of Contents
  • Dataset Description
    • Dataset Summary
    • Languages
  • Dataset Structure
    • Data Instances
      • base
      • original
    • Data Fields
      • base
      • original
    • Data Splits
    • Annotations
  • Additional Information
    • Licensing Information
    • Citation Information
    • Contributions

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.

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', 'sentence_A_En', 'sentence_B_En', 'entailment_label_En', 'relatedness_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', 'sentence_A_En', 'sentence_B_En', 'entailment_label_En', 'relatedness_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="original")
print(dataset)
# DatasetDict({
#     train: Dataset({
#         features: ['id', 'premise', 'hypothesis', 'label', 'score', 'sentence_A_En', 'sentence_B_En', 'entailment_label_En', 'relatedness_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', 'sentence_A_En', 'sentence_B_En', 'entailment_label_En', 'relatedness_score_En', 'corr_entailment_labelAB_En', 'corr_entailment_labelBA_En', 'image_ID', 'original_caption', 'semtag_short', 'semtag_long'],
#         num_rows: 4927
#     })
# })

base

An example of looks as follows:

{
  '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:

{
  '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.

• id: The number of the sentence pair.
• premise: The premise (sentence_A_Ja).
• hypothesis: The hypothesis (sentence_B_Ja).
• label: The correct label for this 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.

• 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
• Hitomi Yanaka, Koji Mineshima, Assessing the Generalization Capacity of Pre-trained Language Models through Japanese Adversarial Natural Language Inference, Proceedings of the 2021 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP2021), 2021.

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.