数据集:
deal_or_no_dialog
任务:
对话语言:
en计算机处理:
monolingual大小:
10K<n<100K语言创建人:
crowdsourced批注创建人:
crowdsourced源数据集:
original预印本库:
arxiv:1706.05125许可:
cc-by-4.0A large dataset of human-human negotiations on a multi-issue bargaining task, where agents who cannot observe each other’s reward functions must reach an agreement (or a deal) via natural language dialogue.
Train end-to-end models for negotiation
The text in the dataset is in English
{'dialogue': 'YOU: i love basketball and reading THEM: no . i want the hat and the balls YOU: both balls ? THEM: yeah or 1 ball and 1 book YOU: ok i want the hat and you can have the rest THEM: okay deal ill take the books and the balls you can have only the hat YOU: ok THEM: ', 'input': {'count': [3, 1, 2], 'value': [0, 8, 1]}, 'output': 'item0=0 item1=1 item2=0 item0=3 item1=0 item2=2', 'partner_input': {'count': [3, 1, 2], 'value': [1, 3, 2]}}
dialogue : The dialogue between the agents. input : The input of the firt agent. partner_input : The input of the other agent. count : The count of the three available items. value : The value of the three available items. output : Describes how many of each of the three item typesare assigned to each agent
train | validation | test | |
---|---|---|---|
dialogues | 10095 | 1087 | 1052 |
self_play | 8172 | NA | NA |
[More Information Needed]
[More Information Needed]
Who are the source language producers?[More Information Needed]
[More Information Needed]
Who are the annotators?Human workers using Amazon Mechanical Turk. They were paid $0.15 per dialogue, with a $0.05 bonus for maximal scores. Only workers based in the United States with a 95% approval rating and at least 5000 previous HITs were used.
[More Information Needed]
[More Information Needed]
[More Information Needed]
[More Information Needed]
[More Information Needed]
The project is licenced under CC-by-NC
@article{lewis2017deal, title={Deal or no deal? end-to-end learning for negotiation dialogues}, author={Lewis, Mike and Yarats, Denis and Dauphin, Yann N and Parikh, Devi and Batra, Dhruv}, journal={arXiv preprint arXiv:1706.05125}, year={2017} }
Thanks to @moussaKam for adding this dataset.