Update README.md

README.md

@@ -62,37 +62,6 @@ dataset_info:
     num_examples: 120
   download_size: 216429
   dataset_size: 388263
-- config_name: v1.1_no_time_dependent
-  features:
-  - name: qid
-    dtype: string
-  - name: type
-    dtype: string
-  - name: question
-    dtype: string
-  - name: answer
-    dtype: string
-  - name: derivations
-    sequence:
-    - name: '0'
-      dtype: string
-    - name: '1'
-      dtype: string
-    - name: '2'
-      sequence: string
-  - name: page_ids
-    sequence: string
-  - name: time_dependent
-    dtype: bool
-  splits:
-  - name: train
-    num_bytes: 336795.52407932014
-    num_examples: 1020
-  - name: validation
-    num_bytes: 37625.25
-    num_examples: 117
-  download_size: 207757
-  dataset_size: 374420.77407932014
 configs:
 - config_name: v1
   data_files:
@@ -106,10 +75,40 @@ configs:
     path: v1.1/train-*
   - split: validation
     path: v1.1/validation-*
-- config_name: v1.1_no_time_dependent
-  data_files:
-  - split: train
-    path: v1.1_no_time_dependent/train-*
-  - split: validation
-    path: v1.1_no_time_dependent/validation-*
 ---
+
+# JEMHopQA
+
+> JEMHopQA (Japanese Explainable Multi-hop Question Answering) is a Japanese multi-hop QA dataset that can evaluate internal reasoning. It is a task that takes a question as input and generates an answer and derivations. Derivations are a set of derivation steps and is a semi-structured representation of relationships between entities. This dataset contains both compositional (linking information from two Wikipedia articles) and comparison (comparing information from two Wikipedia articles) questions.
+
+Source: [JEMHopQA on GitHub](https://github.com/aiishii/JEMHopQA)
+
+
+### Licensing Information
+
+[Creative Commons Attribution Share Alike 4.0 International](https://github.com/aiishii/JEMHopQA/blob/main/LICENSE)
+
+### Citation Information
+
+```
+@inproceedings{ishii-etal-2024-jemhopqa-dataset,
+    title = "{JEMH}op{QA}: Dataset for {J}apanese Explainable Multi-Hop Question Answering",
+    author = "Ishii, Ai  and
+      Inoue, Naoya  and
+      Suzuki, Hisami  and
+      Sekine, Satoshi",
+    editor = "Calzolari, Nicoletta  and
+      Kan, Min-Yen  and
+      Hoste, Veronique  and
+      Lenci, Alessandro  and
+      Sakti, Sakriani  and
+      Xue, Nianwen",
+    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
+    month = may,
+    year = "2024",
+    address = "Torino, Italia",
+    publisher = "ELRA and ICCL",
+    url = "https://aclanthology.org/2024.lrec-main.831",
+    pages = "9515--9525",
+}
+```