Datasets:
Size:
10K<n<100K
License:
| 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](#dataset-card-for-janli) | |
| - [Table of Contents](#table-of-contents) | |
| - [Dataset Description](#dataset-description) | |
| - [Dataset Summary](#dataset-summary) | |
| - [Japanese Sentences Involving Compositional Knowledge (JSICK) Dataset.](#japanese-sentences-involving-compositional-knowledge-jsick-dataset) | |
| - [JSICK-stress Test set](#jsick-stress-test-set) | |
| - [Languages](#languages) | |
| - [Dataset Structure](#dataset-structure) | |
| - [Data Instances](#data-instances) | |
| - [base](#base) | |
| - [stress](#stress) | |
| - [Data Fields](#data-fields) | |
| - [base](#base-1) | |
| - [stress](#stress-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/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](https://github.com/verypluming/JSICK): | |
| #### 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)](https://aclanthology.org/L14-1314/) 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: | |
| ```python | |
| 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: | |
| ```json | |
| { | |
| '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: | |
| ```json | |
| { | |
| '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](https://www.kaggle.com/datasets/adityajn105/flickr8k) | | |
| | original_caption | original caption in [8K ImageFlickr dataset](https://www.kaggle.com/datasets/adityajn105/flickr8k) | | |
| | corr_entailment_labelAB_En | corrected entailment label from A to B in English by [(Karouli et al., 2017)](http://vcvpaiva.github.io/includes/pubs/2017-iwcs.pdf) | | |
| | corr_entailment_labelBA_En | corrected entailment label from B to A in English by [(Karouli et al., 2017)](http://vcvpaiva.github.io/includes/pubs/2017-iwcs.pdf) | | |
| #### 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 | validation | 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 | |
| - [verypluming/JaNLI](https://github.com/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.](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 for creating this dataset. |