:memo: Update documents

README.md

@@ -1,3 +1,215 @@
 ---
-license: cc-by-sa-4.0
+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)
+    - [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/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.
+
+### 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', '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/jsick", 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.
+
+- `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](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.

jsick.py

@@ -17,7 +17,10 @@ _CITATION = """\
 """
 
 _DESCRIPTION = """\
-
+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.
+(from official website)
 """
 
 _HOMEPAGE = "https://github.com/verypluming/JSICK"
@@ -51,7 +54,7 @@ class JSICKDataset(ds.GeneratorBasedBuilder):
             description="fuga",
         ),
         ds.BuilderConfig(
-            name="stress_original",
+            name="stress-original",
             version=VERSION,
             description="fuga",
         ),