--- annotations_creators: - crowdsourced language_creators: - crowdsourced languages: - en licenses: - cc-by-4-0 multilinguality: - monolingual size_categories: - 10K 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]}} ### Data Fields `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 ### Data Splits | | train | validation | test | |------------|------:|-----------:|-----:| | dialogues | 10095 | 1087 | 1052 | | self_play | 8172 | NA | NA | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [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. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information The project is licenced under CC-by-NC ### Citation Information ``` @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} } ``` ### Contributions Thanks to [@moussaKam](https://github.com/moussaKam) for adding this dataset.