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  1. README.md +213 -1
  2. jsick.py +5 -2
README.md CHANGED
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  ---
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- license: cc-by-sa-4.0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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+ annotations_creators:
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+ - expert-generated
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+ language:
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+ - ja
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+ - en
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+ language_creators:
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+ - expert-generated
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+ license:
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+ - cc-by-sa-4.0
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+ multilinguality:
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+ - translation
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+ pretty_name: JSICK
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+ size_categories:
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+ - 1K<n<10K
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+ source_datasets:
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+ - extended|sick
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+ tags:
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+ - semantic-textual-similarity
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+ - sts
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+ task_categories:
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+ - sentence-similarity
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+ - text-classification
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+ task_ids:
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+ - natural-language-inference
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+ - semantic-similarity-scoring
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  ---
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+
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+ # Dataset Card for JaNLI
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+
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+ ## Table of Contents
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+ - [Dataset Card for JaNLI](#dataset-card-for-janli)
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+ - [Table of Contents](#table-of-contents)
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+ - [Dataset Description](#dataset-description)
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+ - [Dataset Summary](#dataset-summary)
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+ - [Languages](#languages)
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+ - [Dataset Structure](#dataset-structure)
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+ - [Data Instances](#data-instances)
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+ - [base](#base)
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+ - [original](#original)
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+ - [Data Fields](#data-fields)
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+ - [base](#base-1)
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+ - [original](#original-1)
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+ - [Data Splits](#data-splits)
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+ - [Annotations](#annotations)
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+ - [Additional Information](#additional-information)
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+ - [Licensing Information](#licensing-information)
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+ - [Citation Information](#citation-information)
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+ - [Contributions](#contributions)
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+
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+ ## Dataset Description
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+
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+ - **Homepage:** https://github.com/verypluming/JSICK
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+ - **Repository:** https://github.com/verypluming/JSICK
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+ - **Paper:** https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00518/113850/Compositional-Evaluation-on-Japanese-Textual
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+ - **Paper:** https://www.jstage.jst.go.jp/article/pjsai/JSAI2021/0/JSAI2021_4J3GS6f02/_pdf/-char/ja
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+
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+ ### Dataset Summary
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+
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+ From official [GitHub](https://github.com/verypluming/JSICK):
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+
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+ Japanese Sentences Involving Compositional Knowledge (JSICK) Dataset.
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+ 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.
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+ We hope that our dataset will be useful in research for realizing more advanced models that are capable of appropriately performing multilingual compositional inference.
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+
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+ ### Languages
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+
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+ The language data in JSICK is in Japanese and English.
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+
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+
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+ ## Dataset Structure
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+
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+
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+ ### Data Instances
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+ When loading a specific configuration, users has to append a version dependent suffix:
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+
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+ ```python
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+ import datasets as ds
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+
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+ dataset: ds.DatasetDict = ds.load_dataset("hpprc/jsick")
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+ print(dataset)
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+ # DatasetDict({
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+ # train: Dataset({
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+ # features: ['id', 'premise', 'hypothesis', 'label', 'heuristics', 'number_of_NPs', 'semtag'],
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+ # num_rows: 13680
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+ # })
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+ # test: Dataset({
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+ # features: ['id', 'premise', 'hypothesis', 'label', 'heuristics', 'number_of_NPs', 'semtag'],
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+ # num_rows: 720
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+ # })
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+ # })
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+
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+ dataset: ds.DatasetDict = ds.load_dataset("hpprc/jsick", name="original")
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+ print(dataset)
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+ # DatasetDict({
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+ # train: Dataset({
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+ # features: ['id', 'sentence_A_Ja', 'sentence_B_Ja', 'entailment_label_Ja', 'heuristics', 'number_of_NPs', 'semtag'],
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+ # num_rows: 13680
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+ # })
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+ # test: Dataset({
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+ # features: ['id', 'sentence_A_Ja', 'sentence_B_Ja', 'entailment_label_Ja', 'heuristics', 'number_of_NPs', 'semtag'],
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+ # num_rows: 720
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+ # })
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+ # })
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+ ```
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+
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+
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+ #### base
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+
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+ An example of looks as follows:
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+
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+ ```json
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+ {
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+ 'id': 12,
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+ 'premise': 'θ‹₯θ€…γŒγƒ•γƒƒγƒˆγƒœγƒΌγƒ«ιΈζ‰‹γ‚’θ¦‹γ¦γ„γ‚‹',
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+ 'hypothesis': 'γƒ•γƒƒγƒˆγƒœγƒΌγƒ«ιΈζ‰‹γ‚’θ‹₯θ€…γŒθ¦‹γ¦γ„γ‚‹',
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+ 'label': 0,
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+ 'heuristics': 'overlap-full',
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+ 'number_of_NPs': 2,
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+ 'semtag': 'scrambling'
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+ }
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+ ```
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+
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+ #### original
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+
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+ An example of looks as follows:
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+
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+ ```json
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+ {
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+ 'id': 12,
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+ 'sentence_A_Ja': 'θ‹₯θ€…γŒγƒ•γƒƒγƒˆγƒœγƒΌγƒ«ιΈζ‰‹γ‚’θ¦‹γ¦γ„γ‚‹',
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+ 'sentence_B_Ja': 'γƒ•γƒƒγƒˆγƒœγƒΌγƒ«ιΈζ‰‹γ‚’θ‹₯θ€…γŒθ¦‹γ¦γ„γ‚‹',
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+ 'entailment_label_Ja': 0,
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+ 'heuristics': 'overlap-full',
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+ 'number_of_NPs': 2,
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+ 'semtag': 'scrambling'
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+ }
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+ ```
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+
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+ ### Data Fields
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+
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+ #### base
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+
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+ A version adopting the column names of a typical NLI dataset.
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+
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+ - `id`: The number of the sentence pair.
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+ - `premise`: The premise (sentence_A_Ja).
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+ - `hypothesis`: The hypothesis (sentence_B_Ja).
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+ - `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).
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+ - `heuristics`: The heuristics (structural pattern) tag. The tags are: subsequence, constituent, full-overlap, order-subset, and mixed-subset.
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+ - `number_of_NPs`: The number of noun phrase in a sentence.
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+ - `semtag`: The linguistic phenomena tag.
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+
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+ #### original
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+
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+ The original version retaining the unaltered column names.
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+
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+ - `id`: The number of the sentence pair.
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+ - `sentence_A_Ja`: The premise.
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+ - `sentence_B_Ja`: The hypothesis.
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+ - `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
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+ - `heuristics`: The heuristics (structural pattern) tag. The tags are: subsequence, constituent, full-overlap, order-subset, and mixed-subset.
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+ - `number_of_NPs`: The number of noun phrase in a sentence.
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+ - `semtag`: The linguistic phenomena tag.
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+
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+
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+ ### Data Splits
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+
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+ | name | train | validation | test |
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+ | -------- | -----: | ---------: | ---: |
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+ | base | 13,680 | | 720 |
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+ | original | 13,680 | | 720 |
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+
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+
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+
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+ ### Annotations
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+
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+ 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.
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+ 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.
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+ Additionally, 11 categories of Japanese linguistic phenomena and constructions are focused on for generating the five patterns of adversarial inferences.
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+
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+ For each linguistic phenomenon, a template for the premise sentence P is fixed, and multiple templates for hypothesis sentences H are created.
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+ In total, 144 templates for (P, H) pairs are produced.
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+ 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.
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+
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+ The JaNLI dataset is generated by instantiating each template 100 times, resulting in a total of 14,400 examples.
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+ The same number of entailment and non-entailment examples are generated for each phenomenon.
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+ 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.
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+ The dataset uses a total of 158 words (nouns and verbs), which occur more than 20 times in the JSICK and JSNLI datasets.
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+
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+
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+ ## Additional Information
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+
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+ - [verypluming/JaNLI](https://github.com/verypluming/JaNLI)
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+ - [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/)
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+
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+ ### Licensing Information
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+
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+ CC BY-SA 4.0
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+
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+ ### Citation Information
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+
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+ ```bibtex
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+ @InProceedings{yanaka-EtAl:2021:blackbox,
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+ author = {Yanaka, Hitomi and Mineshima, Koji},
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+ title = {Assessing the Generalization Capacity of Pre-trained Language Models through Japanese Adversarial Natural Language Inference},
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+ booktitle = {Proceedings of the 2021 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP2021)},
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+ url = {https://aclanthology.org/2021.blackboxnlp-1.26/},
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+ year = {2021},
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+ }
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+ ```
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+
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+ ### Contributions
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+
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+ Thanks to [Hitomi Yanaka](https://hitomiyanaka.mystrikingly.com/) and Koji Mineshima for creating this dataset.
jsick.py CHANGED
@@ -17,7 +17,10 @@ _CITATION = """\
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  """
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  _DESCRIPTION = """\
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-
 
 
 
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  """
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  _HOMEPAGE = "https://github.com/verypluming/JSICK"
@@ -51,7 +54,7 @@ class JSICKDataset(ds.GeneratorBasedBuilder):
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  description="fuga",
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  ),
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  ds.BuilderConfig(
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- name="stress_original",
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  version=VERSION,
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  description="fuga",
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  ),
 
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  """
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  _DESCRIPTION = """\
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+ Japanese Sentences Involving Compositional Knowledge (JSICK) Dataset.
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+ JSICK is the Japanese NLI and STS dataset by manually translating the English dataset SICK (Marelli et al., 2014) into Japanese.
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+ We hope that our dataset will be useful in research for realizing more advanced models that are capable of appropriately performing multilingual compositional inference.
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+ (from official website)
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  """
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  _HOMEPAGE = "https://github.com/verypluming/JSICK"
 
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  description="fuga",
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  ),
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  ds.BuilderConfig(
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+ name="stress-original",
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  version=VERSION,
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  description="fuga",
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  ),