Update README.md

README.md

@@ -72,15 +72,20 @@ dataset_info:
         - name: value
           dtype: string
       - name: db_result
-        sequence:
+        struct:
         - name: candidate_entities
-          sequence: string
+          sequence:
+            dtype: string
+            id: entity_name
+          id: candidate_entities
         - name: active_entity
           sequence:
           - name: slot
             dtype: string
+            id: active_entity/slot
           - name: value
             dtype: string
+            id: active_entity/value
       - name: book_result
         sequence:
         - name: domain
@@ -91,14 +96,145 @@ dataset_info:
           dtype: string
   splits:
   - name: train
-    num_bytes: 56760986
+    num_bytes: 60731411
     num_examples: 3646
   - name: validation
-    num_bytes: 4667253
+    num_bytes: 5000420
     num_examples: 300
   - name: test
-    num_bytes: 4748370
+    num_bytes: 5085276
     num_examples: 300
   download_size: 11016438
-  dataset_size: 66176609
+  dataset_size: 70817107
 ---
+
+# Dataset Card for JMultiWOZ
+
+## Dataset Description
+
+- **Repository:** [nu-dialouge/jmultiwoz](https://github.com/nu-dialogue/jmultiwoz)
+- **Paper:** [JMultiWOZ: A Large-Scale Japanese Multi-Domain Task-Oriented Dialogue Dataset]()
+- **Point of Contact:** [Atsumoto Ohashi]([email protected])
+
+### Dataset Summary
+
+JMultiWOZ is a large-scale Japanese multi-domain task-oriented dialogue dataset. The dataset is collected using the Wizard-of-Oz (WoZ) methodology, where two human annotators simulate the user and the system. The dataset contains 4,246 dialogues across 6 domains, including restaurant, hotel, attraction, shopping, taxi, and weather. Available annotations include user goal, dialogue state, and utterances.
+
+### Supported Tasks
+
+- **Dialogue State Tracking**: The dataset can be used to train models for dialogue state tracking, which is the task of predicting the user's belief state at each turn in the dialogue.
+- **Dialogue Generation**: The dataset can be used to train models for dialogue generation, which is the task of generating a response given the dialogue history.
+
+### Languages
+
+The text in the dataset is in Japanese (`ja`).
+
+## Dataset Usage
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("nu-dialogue/jmultiwoz", trust_remote_code=True)
+```
+
+## Dataset Structure
+
+### Data Instances
+
+A data instance is a full multi-turn dialogue between a `USER` and a `SYSTEM`. Each turn has an `utterance`:
+```json
+[
+  "福岡へ行くよていなのですが、値段が普通くらいの宿泊施設を探してもらっていいですか？",
+  "かしこまりました。ではWITH THE STYLE FUKUOKAはいかがでしょうか。"
+]
+```
+
+`SYSTEM` turn also has a `dialogue_state` which contains `belief_state`, `book_state`, `db_result`, and `book_result`:
+
+`belief_state`:
+```json
+{
+  "domain": ["general", "general", "hotel", ...],
+  "slot": ["active_domain", "city", "pricerange", ...],
+  "value": ["hotel", "福岡", "普通", ...]
+}
+```
+
+`book_state`:
+```json
+{
+  "domain": ["hotel", "hotel", "hotel", ...],
+  "slot": ["people", "day", "stay", ...],
+  "value": [None, None, None, ...]
+}
+```
+
+`db_result`:
+```json
+{
+  "candidate_entities": ["WITH THE STYLE FUKUOKA", "ANA クラウンプラザホテル福岡", ...],
+  "active_entity": {
+    "slot": ["city", "name", "genre", ...],
+    "value": ["福岡", "WITH THE STYLE FUKUOKA", "リゾートホテル", ...]
+}
+```
+
+### Data Fields
+
+Each dialogue instance has the following fields:
+- `dialogue_id` (int32): A unique identifier for the dialogue.
+- `dialogue_name` (string): A name for the dialogue.
+- `system_name` (string): The name of the wizard.
+- `user_name` (string): The name of the user.
+- `goal` (sequence): The user's goal for the dialogue.
+  - `domain` (string): The domain of the goal.
+  - `task` (string): The task of the goal.
+  - `slot` (string): The slot of the goal.
+  - `value` (string): The value of the goal.
+- `goal_description` (sequence): A description of the user's goal.
+  - `domain` (string): The domain of the goal.
+  - `text` (string): The text of the goal.
+- `turns` (sequence): The turns in the dialogue.
+  - `turn_id` (int32): A unique identifier for the turn.
+  - `speaker` (string): The speaker of the turn.
+  - `utterance` (string): The utterance of the turn.
+  - `dialogue_state` (struct): The dialogue state of the turn.
+    - `belief_state` (sequence): The belief state of the turn.
+      - `domain` (string): The domain of the belief state.
+      - `slot` (string): The slot of the belief state.
+      - `value` (string): The value of the belief state.
+    - `book_state` (sequence): The book state of the turn.
+      - `domain` (string): The domain of the book state.
+      - `slot` (string): The slot of the book state.
+      - `value` (string): The value of the book state.
+    - `db_result` (struct): The database result of the turn.
+      - `candidate_entities` (sequence): The candidate entities of the database result.
+        - `entity_name` (string): The name of the entity.
+      - `active_entity` (sequence): The active entity of the database result.
+        - `slot` (string): The slot of the active entity.
+        - `value` (string): The value of the active entity.
+    - `book_result` (sequence): The book result of the turn.
+      - `domain` (string): The domain of the book result.
+      - `success` (string): The success of the book result.
+      - `ref` (string): The reference of the book result.
+
+### Data Splits
+
+The dataset is split into a train, validation, and test split with the following sizes:
+
+|                     | train   | validation  | test  |
+|---                  | ---:    | ---:        | ---:  |
+| Number of dialogues | 3646    | 300         | 300   |
+| Number of turns     | 52,405  | 4,346       | 4,435 |
+
+
+## Citation Information
+```bibtex
+@inproceedings{ohashi-etal-2024-jmultiwoz,
+    title = "JMultiWOZ: A Large-Scale Japanese Multi-Domain Task-Oriented Dialogue Dataset",
+    author = "Ohashi, Atsumoto and Hirai, Ryu and Iizuka, Shinya and Higashinaka, Ryuichiro",
+    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation",
+    year = "2024",
+    url = "",
+    pages = "",
+}
+```