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README.md

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+---
+language:
+- en
+license: []
+multilinguality:
+- monolingual
+pretty_name: MetaLWOZ
+size_categories:
+- 10K<n<100K
+task_categories:
+- conversational
+---
+
 # Dataset Card for MetaLWOZ
 
 - **Repository:** https://www.microsoft.com/en-us/research/project/metalwoz/
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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('metalwoz')
+ontology = load_ontology('metalwoz')
+database = load_database('metalwoz')
+```
+For more usage please refer to [here](https://github.com/ConvLab/ConvLab-3/tree/master/data/unified_datasets).
+
 ### Dataset Summary
 
 This large dataset was created by crowdsourcing 37,884 goal-oriented dialogs, covering 227 tasks in 47 domains. Domains include bus schedules, apartment search, alarm setting, banking, and event reservation. Each dialog was grounded in a scenario with roles, pairing a person acting as the bot and a person acting as the user. (This is the Wizard of Oz reference—using people behind the curtain who act as the machine). Each pair were given a domain and a task, and instructed to converse for 10 turns to satisfy the user’s queries. For example, if a user asked if a bus stop was operational, the bot would respond that the bus stop had been moved two blocks north, which starts a conversation that addresses the user’s actual need.