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TaskMaster1
创建来自Hello Dataset / Robert
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Overview

The dataset consists of 13,215 task-based dialogs, including 5,507 spoken and 7,708 written dialogs created with two distinct procedures. Each conversation falls into one of six domains: ordering pizza, creating auto repair appointments, setting up ride service, ordering movie tickets, ordering coffee drinks and making restaurant reservations.

Data Collection

Two-person, spoken dialogs were created using a Wizard of Oz methodology in which crowdsourced workers played the role of a 'user' and trained call center operators played the role of the 'assistant'. In this way, users were led to believe they were interacting with an automated system while it was in fact a human. As a result, users could express their turns in natural ways but in the context of an automated interface. For the written dialogs, we engaged crowdsourced workers to write the full conversation themselves based on scenarios outlined for each task, thereby playing roles of both the user and assistant. In a departure from traditional annotation techniques, dialogs are labeled with simple API arguments, i.e. the slot values required to execute the task transaction, instead of traditional semantic intents and dialog acts.

Citation

Please use the following citation when referencing the dataset:

@inproceedings{byrne-etal-2019-taskmaster,
    title = {Taskmaster-1:Toward a Realistic and Diverse Dialog Dataset},
    author = {Bill Byrne and Karthik Krishnamoorthi and Chinnadhurai Sankar and Arvind Neelakantan
and Daniel Duckworth and Semih Yavuz and Ben Goodrich and Amit Dubey and Kyu-Young Kim and
Andy Cedilnik},
    booktitle = {2019 Conference on Empirical Methods in Natural Language
Processing and 9th International Joint Conference on Natural Language Processing},
    address = {Hong Kong},
    year = {2019}
    }

License

CC BY 4.0

数据集信息
应用场景NLP
标注类型Text
LicenseCC BY 4.0
更新时间2021-03-24 22:52:12
数据概要
数据格式Text
数据数量13.21k
已标注数量0
文件大小10KB
版权归属方
Google Research
标注方
未知
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