Update README.md

README.md

@@ -20,7 +20,9 @@ task_ids:
 - dialogue-modeling
 ---
 # Dataset Card for RiSAWOZ
+
 ## Table of Contents
+
 - [Dataset Card for RiSAWOZ](#dataset-card-for-risawoz)
   - [Table of Contents](#table-of-contents)
   - [Dataset Description](#dataset-description)
@@ -45,25 +47,37 @@ task_ids:
     - [Licensing Information](#licensing-information)
     - [Citation Information](#citation-information)
     - [Contributions](#contributions)
+
 ## Dataset Description
-- **Homepage:** https://terryqj0107.github.io/RiSAWOZ_webpage
-- **Repository:** https://github.com/terryqj0107/RiSAWOZ
-- **Paper:** https://aclanthology.org/2020.emnlp-main.67.pdf
+
+- **Homepage:** <https://terryqj0107.github.io/RiSAWOZ_webpage>
+- **Repository:** <https://github.com/terryqj0107/RiSAWOZ>
+- **Paper:** <https://aclanthology.org/2020.emnlp-main.67.pdf>
 - **Point of Contact:** Deyi Xiong (dyxiong@tju.edu.cn)
+
 ### Dataset Summary
+
 RiSAWOZ contains 11.2K human-to-human (H2H) multiturn semantically annotated dialogues, with more than 150K utterances spanning over 12 domains, which is larger than all previous annotated H2H conversational datasets. Both single- and multi-domain dialogues are constructed, accounting for 65% and 35%, respectively.
+
 ### Supported Tasks and Leaderboards
+
 - Natural Language Understanding
 - Dialogue State Tracking
 - Dialogue Context-to-Text Generation
 - Coreference Resolution
 - Unified Generative Ellipsis and Coreference Resolution
+
 ### Languages
-Mandarin Chinese
+
+Mandarin Chinese (zh-CN)
+
 ## Dataset Structure
+
 ### Data Instances
+
 A JSON formatted example of a typical instance in RiSAWOZ dataset:
-```
+
+```JSON
 {
     "dialogue_id": "attraction_goal_4-63###6177",
     "goal": "attraction_goal_4-63: 你是苏州人，但不怎么出去玩。你朋友来苏州找你，你准备带他逛逛“水乡古镇”，你希望客服给你推荐个消费水平“中等”的地方。然后你要问清楚这地方“是否地铁直达”、“特点”、“门票价格”这些信息。最后，你要感谢客服的帮助，然后说再见。",
@@ -577,66 +591,93 @@ A JSON formatted example of a typical instance in RiSAWOZ dataset:
     ]
 }
 ```
+
 ### Data Fields
+
 The data fields are the same among all splits.
+
 - dialogue_id (string): dialogue ID
 - goal (string): natural language descriptions of the user goal
 - domains (list of strings): domains mentioned in current dialogue session
 - dialogue (list of dicts): dialog turns and corresponding annotations. Each turn includes:
-    - turn_id (int): turn ID
-    - turn_domain (list of strings): domain mentioned in current turn
-    - user_utterance (string): user utterance
-    - system_utterance (string): system utterance
-    - belief_state (dict): dialogue state, including:
-        - inform slot-values (dict): the slots and corresponding values informed until current turn
-        - turn_inform (dict): the slots and corresponding values informed in current turn
-        - turn request (dict): the slots requested in current turn
-    - user_actions (list of lists): user dialogue acts in current turn
-    - user_actions (list of lists): system dialogue acts in current turn
-    - db_results (list of strings): database search results
-    - segmented_user_utterance (string): word segmentation result of user utterance
-    - segmented_system_utterance (string): word segmentation result of system utterance
+  - turn_id (int): turn ID
+  - turn_domain (list of strings): domain mentioned in current turn
+  - user_utterance (string): user utterance
+  - system_utterance (string): system utterance
+  - belief_state (dict): dialogue state, including:
+    - inform slot-values (dict): the slots and corresponding values informed until current turn
+    - turn_inform (dict): the slots and corresponding values informed in current turn
+    - turn request (dict): the slots requested in current turn
+  - user_actions (list of lists): user dialogue acts in current turn
+  - user_actions (list of lists): system dialogue acts in current turn
+  - db_results (list of strings): database search results
+  - segmented_user_utterance (string): word segmentation result of user utterance
+  - segmented_system_utterance (string): word segmentation result of system utterance
+
 ### Data Splits
+
 |            |  train | validation | test  |
 | ---------  | -----: | ---------: | ----: |
 | #dialogues | 10000  |        600 |   600 |
 | #turns     | 134580 |       8116 |  9286 |
 | Avg. turns | 13.5   |       13.5 |  15.5 |
+
 ## Dataset Creation
+
 ### Curation Rationale
+
 Gather human-to-human dialog in Chinese.
+
 ### Communicative Goal
+
 Generate system response given dialogue context across multiple domains.
+
 ### Annotations
+
 #### Annotation process
+
 - Does the dataset have additional annotations for each instance?
-    - crowd-sourced
+  - crowd-sourced
 - What is the number of raters
-    - 51<n<100
+  - 51<n<100
 - Describe the qualifications required of an annotator.
-    - Chinese native speaker
+  - Chinese native speaker
 - How many annotators saw each training example?
-    - 3
+  - 3
 - How many annotators saw each test example?
-    - 3
+  - 3
+
 ### Personal and Sensitive Information
+
 The slots and values as well as utterances do not contain any personal information.
+
 ## Considerations for Using the Data
+
 ### Social Impact of Dataset
+
 RiSAWOZ is the first large-scale multi-domain Chinese Wizard-of-Oz dataset with rich semantic annotations.
+
 ## Additional Information
+
 ### Dataset Curators
+
 - Jun Quan (Soochow University, Suzhou, China)
 - Shian Zhang (Soochow University, Suzhou, China)
 - Qian Cao (Soochow University, Suzhou, China)
 - Zizhong Li (Tianjin University, Tianjin, China)
 - Deyi Xiong (Tianjin University, Tianjin, China)
+
 ### Funding Information
+
 the National Key Research and Development Project
+
 ### Licensing Information
+
 cc-by-4.0: Creative Commons Attribution 4.0 International
+
 ### Citation Information
-```
+
+```BibTeX
 @inproceedings{quan-etal-2020-risawoz,
     title = "{R}i{SAWOZ}: A Large-Scale Multi-Domain {W}izard-of-{O}z Dataset with Rich Semantic Annotations for Task-Oriented Dialogue Modeling",
     author = "Quan, Jun  and
@@ -653,5 +694,7 @@ cc-by-4.0: Creative Commons Attribution 4.0 International
     pages = "930--940",
 }
 ```
+
 ### Contributions
-Thanks to Tianhao Shen, Chaobin You and Deyi Xiong for adding this dataset to GEM.
+
+Thanks to Tianhao Shen, Chaobin You and Deyi Xiong for adding this dataset to GEM.