Data Card.

README.md

@@ -1407,7 +1407,7 @@ no
 
 <!-- info: Getting started with in-depth research on the task. Add relevant pointers to resources that researchers can consult when they want to get started digging deeper into the task. -->
 <!-- scope: microscope -->
-https://terryqj0107.github.io/RiSAWOZ_webpage
+[Website](https://terryqj0107.github.io/RiSAWOZ_webpage)
 
 #### Technical Terms
 
@@ -1663,7 +1663,7 @@ yes
 
 <!-- info: Describe the original dataset's maintenance plan. -->
 <!-- scope: microscope -->
-Building a leaderboard webpage to trace and display the latest results on the dataset (done, https://terryqj0107.github.io/RiSAWOZ_webpage/)
+Building a leaderboard webpage to trace and display the latest results on the [dataset](https://terryqj0107.github.io/RiSAWOZ_webpage/)
 
 #### Maintainer Contact Information
 

RiSAWOZ.json

@@ -100,7 +100,7 @@
     },
     "maintenance": {
       "has-maintenance": "yes",
-      "description": "Building a leaderboard webpage to trace and display the latest results on the dataset (done, https://terryqj0107.github.io/RiSAWOZ_webpage/)",
+      "description": "Building a leaderboard webpage to trace and display the latest results on the [dataset](https://terryqj0107.github.io/RiSAWOZ_webpage/)",
       "contact": "Deyi Xiong ([email protected])",
       "contestation-mechanism": "contact maintainer",
       "contestation-link": "Deyi Xiong ([email protected])",
@@ -124,7 +124,7 @@
       "additional-splits-capacicites": "N/A"
     },
     "starting": {
-      "research-pointers": "https://terryqj0107.github.io/RiSAWOZ_webpage",
+      "research-pointers": "[Website](https://terryqj0107.github.io/RiSAWOZ_webpage)",
       "technical-terms": "- In task-oriented dialogue system, the Natural Language Understanding (NLU) module aims to convert the user utterance into the representation that computer can understand, which includes intent and dialogue act (slot & value) detection.\n- Dialogue State Tracking (DST) is a core component in task-oriented dialogue systems, which extracts dialogue states (user goals) embedded in dialogue context. It has progressed toward open-vocabulary or generation-based DST where state-of-the-art models can generate dialogue states from dialogue context directly.\n- Context-to-Text Generation: encoding dialogue context to decode system response.\n- Coreference Resolution: predict coreference clusters where all mentions are referring to the same entity for each dialogue.\n- Unified Generative Ellipsis and Coreference Resolution: generating omitted or referred expressions from the dialogue context."
     }
   },