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Dataset Card for CraigslistBargains
Dataset Summary
We study negotiation dialogues where two agents, a buyer and a seller, negotiate over the price of an time for sale. We collected a dataset of more than 6K negotiation dialogues over multiple categories of products scraped from Craigslist. Our goal is to develop an agent that negotiates with humans through such conversations. The challenge is to handle both the negotiation strategy and the rich language for bargaining. To this end, we develop a modular framework which separates strategy learning from language generation. Specifically, we learn strategies in a coarse dialogue act space and instantiate that into utterances conditioned on dialogue history.
Supported Tasks and Leaderboards
Languages
This dataset is English
Dataset Structure
Data Instances
{
'agent_info': {
'Bottomline':
[
'None',
'None'
],
'Role':
[
'buyer',
'seller'
],
'Target':
[
7.0,
10.0
]
},
'agent_turn':
[
0,
1,
...
],
'dialogue_acts': {
'intent':
[
'init-price',
'unknown',
...
],
'price':
[
5.0,
-1.0,
...
]
},
'items': {
'Category':
[
'phone',
'phone'
],
'Description':
[
'Charge two devices simultaneously on the go...,
...
],
'Images':
[
'phone/6149527852_0.jpg',
'phone/6149527852_0.jpg'
],
'Price':
[
10.0,
10.0
],
'Title':
[
'Verizon Car Charger with Dual Output Micro USB and ...',
...
]
},
'utterance':
[
'Hi, not sure if the charger would work for my car...'
'It will work...',
...
]
}
Data Fields
agent_info
: Information about each of the agents taking part in the dialogueBottomline
: TBDRole
: Whether the agent is buyer or sellerTarget
: Target price that the buyer/seller wants to hit in the negotiation
agent_turn
: Agent taking the current turn in the dialogue (int
index corresponding toRole
above)dialogue_acts
: Rules-based information about the strategy of each agent for each turnintent
: The intent of the agent at the particular turn (offer, accept, etc.)price
: The current item price associated with the intent and turn in the bargaining process. Default value for missing: (-1
)
items
: Information about the item the agents are bargaining for. Note that there is an elembet for each of the fields below for each agentCategory
: Category of the itemDescription
: Description(s) of the itemImages
: (comma delimited) strings of image names of the itemPrice
: Price(s) of the item. Default value for missing: (-1
)Title
: Title(s) of the item
utterance
: Utterance for each turn in the dialogue, corresponding to the agent inagent_turns
. The utterance may be an empty string (''
) for some turns if multiple dialogue acts take place after an utterance (e.g. there are often multiple dialogue acts associated with the closing of the bargaining process after all utterances have completed to describe the conclusion of the bargaining).
Data Splits
This dataset contains three splits, train
, validation
and test
. Note that test
is not provided with dialogue_acts
information as described above. To ensure schema consistency across dataset splits, the dialogue_acts
field in the test
split is populated with the default values: {"price": -1.0, "intent": ""}
The counts of examples in each split are as follows:
| | Train | Valid | Test | | Input Examples | 5247 | 597 | 838 | | Average Dialogue Length | 9.14 | 9.17 | 9.24 |
Note that
Dataset Creation
From the source paper for this dataset:
To generate the negotiation scenarios, we scraped postings on sfbay.craigslist.org from the 6 most popular categories (housing, furniture, cars, bikes, phones, and electronics). Each posting produces three scenarios with the buyer’s target prices at 0.5x, 0.7x and 0.9x of the listing price. Statistics of the scenarios are shown in Table 2. We collected 6682 human-human dialogues on AMT using the interface shown in Appendix A Figure 2. The dataset statistics in Table 3 show that CRAIGSLISTBARGAIN has longer dialogues and more diverse utterances compared to prior datasets. Furthermore, workers were encouraged to embellish the item and negotiate side offers such as free delivery or pick-up. This highly relatable scenario leads to richer dialogues such as the one shown in Table 1. We also observed various persuasion techniques listed in Table 4 such as embellishment,
Curation Rationale
See Dataset Creation
Source Data
See Dataset Creation
Initial Data Collection and Normalization
See Dataset Creation
Who are the source language producers?
See Dataset Creation
Annotations
If the dataset contains annotations which are not part of the initial data collection, describe them in the following paragraphs.
Annotation process
Annotations for the dialogue_acts
in train
and test
were generated via a rules-based system which can be found in this script
Who are the annotators?
[More Information Needed]
Personal and Sensitive Information
[More Information Needed]
Considerations for Using the Data
[More Information Needed]
Social Impact of Dataset
[More Information Needed]
Discussion of Biases
[More Information Needed]
Other Known Limitations
[More Information Needed]
Additional Information
[More Information Needed]
Dataset Curators
He He and Derek Chen and Anusha Balakrishnan and Percy Liang
Computer Science Department, Stanford University
{hehe,derekchen14,anusha,pliang}@cs.stanford.edu
The work through which this data was produced was supported by DARPA Communicating with Computers (CwC) program under ARO prime contract no. W911NF15-1-0462
Licensing Information
[More Information Needed]
Citation Information
@misc{he2018decoupling,
title={Decoupling Strategy and Generation in Negotiation Dialogues},
author={He He and Derek Chen and Anusha Balakrishnan and Percy Liang},
year={2018},
eprint={1808.09637},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
Contributions
Thanks to @ZacharySBrown for adding this dataset.
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