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TaskMaster1
Text
NLP
|...
License: CC BY 4.0

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

Data Summary
Type
Text,
Amount
13.215K
Size
9.98MB
Provided by
Google Research
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field. Our researchers publish regularly in academic journals, release projects as open source, and apply research to Google products.
| Amount 13.215K | Size 9.98MB
TaskMaster1
Text
NLP
License: CC BY 4.0

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

0
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