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README.md

@@ -1,3 +1,17 @@
+---
+language:
+- en
+license:
+- cc-by-sa-4.0
+multilinguality:
+- monolingual
+pretty_name: SGD
+size_categories:
+- 10K<n<100K
+task_categories:
+- conversational
+---
+
 # Dataset Card for Schema-Guided Dialogue
 
 - **Repository:** https://github.com/google-research-datasets/dstc8-schema-guided-dialogue
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 - **Leaderboard:** None
 - **Who transforms the dataset:** Qi Zhu(zhuq96 at gmail dot com)
 
+To use this dataset, you need to install [ConvLab-3](https://github.com/ConvLab/ConvLab-3) platform first. Then you can load the dataset via:
+```
+from convlab.util import load_dataset, load_ontology, load_database
+
+dataset = load_dataset('sgd')
+ontology = load_ontology('sgd')
+database = load_database('sgd')
+```
+For more usage please refer to [here](https://github.com/ConvLab/ConvLab-3/tree/master/data/unified_datasets).
+
 ### Dataset Summary
 
 The **Schema-Guided Dialogue (SGD)** dataset consists of over 20k annotated multi-domain, task-oriented conversations between a human and a virtual assistant. These conversations involve interactions with services and APIs spanning 20 domains, such as banks, events, media, calendar, travel, and weather. For most of these domains, the dataset contains multiple different APIs, many of which have overlapping functionalities but different interfaces, which reflects common real-world scenarios. The wide range of available annotations can be used for intent prediction, slot filling, dialogue state tracking, policy imitation learning, language generation, and user simulation learning, among other tasks for developing large-scale virtual assistants. Additionally, the dataset contains unseen domains and services in the evaluation set to quantify the performance in zero-shot or few-shot settings.