ConvLab/tm1

Dataset Card for Taskmaster-1

• Repository: https://github.com/google-research-datasets/Taskmaster/tree/master/TM-1-2019
• Paper: https://arxiv.org/pdf/1909.05358.pdf
• Leaderboard: None
• Who transforms the dataset: Qi Zhu(zhuq96 at gmail dot com)

To use this dataset, you need to install ConvLab-3 platform first. Then you can load the dataset via:

from convlab.util import load_dataset, load_ontology, load_database

dataset = load_dataset('tm1')
ontology = load_ontology('tm1')
database = load_database('tm1')

For more usage please refer to here.

Dataset Summary

The original 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.

• How to get the transformed data from original data:
  • Download master.zip.
  • Run python preprocess.py in the current directory.
• Main changes of the transformation:
  • Remove dialogs that are empty or only contain one speaker.
  • Split woz-dialogs into train/validation/test randomly (8:1:1). The split of self-dialogs is followed the original dataset.
  • Merge continuous turns by the same speaker (ignore repeated turns).
  • Annotate dialogue acts according to the original segment annotations. Add intent annotation (inform/accept/reject). The type of dialogue act is set to non-categorical if the original segment annotation includes a specified slot. Otherwise, the type is set to binary (and the slot and value are empty) since it means general reference to a transaction, e.g. "OK your pizza has been ordered". If there are multiple spans overlapping, we only keep the shortest one, since we found that this simple strategy can reduce the noise in annotation.
  • Add domain, intent, and slot descriptions.
  • Add state by accumulate non-categorical dialogue acts in the order that they appear, except those whose intents are reject.
  • Keep the first annotation since each conversation was annotated by two workers.
• Annotations:
  • dialogue acts, state.

Supported Tasks and Leaderboards

NLU, DST, Policy, NLG

Languages

English

Data Splits

split	dialogues	utterances	avg_utt	avg_tokens	avg_domains	cat slot match(state)	cat slot match(goal)	cat slot match(dialogue act)	non-cat slot span(dialogue act)
train	10535	223322	21.2	8.75	1	-	-	-	100
validation	1318	27903	21.17	8.75	1	-	-	-	100
test	1322	27660	20.92	8.87	1	-	-	-	100
all	13175	278885	21.17	8.76	1	-	-	-	100

6 domains: ['uber_lyft', 'movie_ticket', 'restaurant_reservation', 'coffee_ordering', 'pizza_ordering', 'auto_repair']

• cat slot match: how many values of categorical slots are in the possible values of ontology in percentage.
• non-cat slot span: how many values of non-categorical slots have span annotation in percentage.

Citation

@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} 
}

Licensing Information

CC BY 4.0