metadata

annotations_creators:
  - expert-generated
language_creators:
  - crowdsourced
language:
  - en
license:
  - cc-by-4.0
multilinguality:
  - monolingual
size_categories:
  - 1K<n<10K
source_datasets:
  - original
task_categories:
  - conversational
  - text-generation
  - fill-mask
task_ids:
  - dialogue-modeling
paperswithcode_id: casino
pretty_name: Campsite Negotiation Dialogues
dataset_info:
  features:
    - name: chat_logs
      list:
        - name: text
          dtype: string
        - name: task_data
          struct:
            - name: data
              dtype: string
            - name: issue2youget
              struct:
                - name: Firewood
                  dtype: string
                - name: Water
                  dtype: string
                - name: Food
                  dtype: string
            - name: issue2theyget
              struct:
                - name: Firewood
                  dtype: string
                - name: Water
                  dtype: string
                - name: Food
                  dtype: string
        - name: id
          dtype: string
    - name: participant_info
      struct:
        - name: mturk_agent_1
          struct:
            - name: value2issue
              struct:
                - name: Low
                  dtype: string
                - name: Medium
                  dtype: string
                - name: High
                  dtype: string
            - name: value2reason
              struct:
                - name: Low
                  dtype: string
                - name: Medium
                  dtype: string
                - name: High
                  dtype: string
            - name: outcomes
              struct:
                - name: points_scored
                  dtype: int32
                - name: satisfaction
                  dtype: string
                - name: opponent_likeness
                  dtype: string
            - name: demographics
              struct:
                - name: age
                  dtype: int32
                - name: gender
                  dtype: string
                - name: ethnicity
                  dtype: string
                - name: education
                  dtype: string
            - name: personality
              struct:
                - name: svo
                  dtype: string
                - name: big-five
                  struct:
                    - name: extraversion
                      dtype: float32
                    - name: agreeableness
                      dtype: float32
                    - name: conscientiousness
                      dtype: float32
                    - name: emotional-stability
                      dtype: float32
                    - name: openness-to-experiences
                      dtype: float32
        - name: mturk_agent_2
          struct:
            - name: value2issue
              struct:
                - name: Low
                  dtype: string
                - name: Medium
                  dtype: string
                - name: High
                  dtype: string
            - name: value2reason
              struct:
                - name: Low
                  dtype: string
                - name: Medium
                  dtype: string
                - name: High
                  dtype: string
            - name: outcomes
              struct:
                - name: points_scored
                  dtype: int32
                - name: satisfaction
                  dtype: string
                - name: opponent_likeness
                  dtype: string
            - name: demographics
              struct:
                - name: age
                  dtype: int32
                - name: gender
                  dtype: string
                - name: ethnicity
                  dtype: string
                - name: education
                  dtype: string
            - name: personality
              struct:
                - name: svo
                  dtype: string
                - name: big-five
                  struct:
                    - name: extraversion
                      dtype: float32
                    - name: agreeableness
                      dtype: float32
                    - name: conscientiousness
                      dtype: float32
                    - name: emotional-stability
                      dtype: float32
                    - name: openness-to-experiences
                      dtype: float32
    - name: annotations
      list:
        list: string
  splits:
    - name: train
      num_bytes: 3211407
      num_examples: 1030
  download_size: 1247368
  dataset_size: 3211407
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*

Dataset Card for Casino

Dataset Description
Dataset Structure
Dataset Creation
Considerations for Using the Data
Additional Information

Dataset Description

Repository: Github: Kushal Chawla CaSiNo
Paper: CaSiNo: A Corpus of Campsite Negotiation Dialogues for Automatic Negotiation Systems
Point of Contact: Kushal Chawla

Dataset Summary

We provide a novel dataset (referred to as CaSiNo) of 1030 negotiation dialogues. Two participants take the role of campsite neighbors and negotiate for Food, Water, and Firewood packages, based on their individual preferences and requirements. This design keeps the task tractable, while still facilitating linguistically rich and personal conversations. This helps to overcome the limitations of prior negotiation datasets such as Deal or No Deal and Craigslist Bargain. Each dialogue consists of rich meta-data including participant demographics, personality, and their subjective evaluation of the negotiation in terms of satisfaction and opponent likeness.

Supported Tasks and Leaderboards

Train end-to-end models for negotiation

Languages

English

Dataset Structure

Data Instances

{
    "chat_logs": [
        {
            "text": "Hello! \ud83d\ude42 Let's work together on a deal for these packages, shall we? What are you most interested in?",
            "task_data": {}, 
            "id": "mturk_agent_1"
        },
        ...
    ],
    "participant_info": {
        "mturk_agent_1":
            {
                "value2issue": ...
                "value2reason": ...
                "outcomes": ...
                "demographics": ...
                "personality": ...
            }, 
        "mturk_agent_2": ...
    },
    "annotations": [
        ["Hello! \ud83d\ude42 Let's work together on a deal for these packages, shall we? What are you most interested in?", "promote-coordination,elicit-pref"],
        ...
    ]
}

Data Fields

chat_logs: The negotiation dialogue between two participants
- text: The dialogue utterance
- task_data: Meta-data associated with the utterance such as the deal submitted by a participant
- id: The ID of the participant who typed this utterance
participant_info: Meta-data about the two participants in this conversation
- mturk_agent_1: For the first participant (Note that 'first' is just for reference. There is no order between the participants and any participant can start the conversation)
  - value2issue: The priority order of this participant among Food, Water, Firewood
  - value2reason: The personal arguments given by the participants themselves, consistent with the above preference order. This preference order and these arguments were submitted before the negotiation began.
  - outcomes: The negotiation outcomes for this participant including objective and subjective assessment.
  - demographics: Demographic attributes of the participant in terms of age, gender, ethnicity, and education.
  - personality: Personality attributes for this participant, in terms of Big-5 and Social Value Orientation
- mturk_agent_2: For the second participant; follows the same structure as above
annotations: Strategy annotations for each utterance in the dialogue, wherever available. The first element represents the utterance and the second represents a comma-separated list of all strategies present in that utterance.

Data Splits

No default data split has been provided. Hence, all 1030 data points are under the 'train' split.

	Train
total dialogues	1030
annotated dialogues	396

Dataset Creation

Curation Rationale

The dataset was collected to address the limitations in prior negotiation datasets from the perspective of downstream applications in pedagogy and conversational AI. Please refer to the original paper published at NAACL 2021 for details about the rationale and data curation steps (source paper).

Source Data

Initial Data Collection and Normalization

The dialogues were crowdsourced on Amazon Mechanical Turk. The strategy annotations were performed by expert annotators (first three authors of the paper). Please refer to the original dataset paper published at NAACL 2021 for more details (source paper).

Who are the source language producers?

The primary producers are Turkers on Amazon Mechanical Turk platform. Two turkers were randomly paired with each other to engage in a negotiation via a chat interface. Please refer to the original dataset paper published at NAACL 2021 for more details (source paper).

Annotations

Annotation process

From the source paper for this dataset:

Three expert annotators independently annotated 396 dialogues containing 4615 utterances. The annotation guidelines were iterated over a subset of 5 dialogues, while the reliability scores were computed on a different subset of 10 dialogues. We use the nominal form of Krippendorff’s alpha (Krippendorff, 2018) to measure the inter-annotator agreement. We provide the annotation statistics in Table 2. Although we release all the annotations, we skip Coordination and Empathy for our analysis in this work, due to higher subjectivity resulting in relatively lower reliability scores.

Who are the annotators?

Three expert annotators (first three authors of the paper).

Personal and Sensitive Information

All personally identifiable information about the participants such as MTurk Ids or HIT Ids was removed before releasing the data.

Considerations for Using the Data

Social Impact of Dataset

Please refer to Section 8.2 in the source paper.

Discussion of Biases