Update README.md

README.md

@@ -65,4 +65,62 @@ configs:
   data_files:
   - split: train
     path: template/train-*
+license: cc-by-sa-4.0
+task_categories:
+- text-classification
+language:
+- ja
+tags:
+- nli
+- evaluation
+- benchmark
+pretty_name: >-
+  Jamp: Controlled Japanese Temporal Inference Dataset for Evaluating
+  Generalization Capacity of Language Models
 ---
+
+# Jamp: Controlled Japanese Temporal Inference Dataset for Evaluating Generalization Capacity of Language Models
+
+Jamp([tomo-vv/temporalNLI_dataset](https://github.com/tomo-vv/temporalNLI_dataset)) is the Japanese temporal inference benchmark. 
+This dataset consists of templates, test data, and training data. 
+
+Template subset containing template, time format, or time span in their names are split based on tense fragment, time format, 
+or time span, respectively.
+
+## Dataset Details
+
+### Dataset Description
+
+- **Created by:** tomo-vv([email protected])
+- **Language(s) (NLP):** Japanese
+- **License:** CC BY-SA 4.0
+
+### Dataset Sources
+
+- **Repository:** [tomo-vv/temporalNLI_dataset](https://github.com/tomo-vv/temporalNLI_dataset)
+- **Paper:** [Jamp: Controlled Japanese Temporal Inference Dataset for Evaluating Generalization Capacity of Language Models](https://aclanthology.org/2023.acl-srw.8) (Sugimoto et al., ACL 2023)
+
+## Citation
+
+**BibTeX:**
+
+```
+@inproceedings{sugimoto-etal-2023-jamp,
+    title = "Jamp: Controlled {J}apanese Temporal Inference Dataset for Evaluating Generalization Capacity of Language Models",
+    author = "Sugimoto, Tomoki  and
+      Onoe, Yasumasa  and
+      Yanaka, Hitomi",
+    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)",
+    month = jul,
+    year = "2023",
+    address = "Toronto, Canada",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2023.acl-srw.8",
+    pages = "57--68",
+}
+```
+
+**APA:**
+
+Sugimoto, T., Onoe, Y., & Yanaka, H. (2023). Jamp: Controlled Japanese Temporal Inference Dataset for Evaluating Generalization Capacity of Language Models. 
+arXiv preprint arXiv:2306.10727.