kchawla123/casino
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--- 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 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** [Github: Kushal Chawla CaSiNo](https://github.com/kushalchawla/CaSiNo) - **Paper:** [CaSiNo: A Corpus of Campsite Negotiation Dialogues for Automatic Negotiation Systems](https://aclanthology.org/2021.naacl-main.254.pdf) - **Point of Contact:** [Kushal Chawla](kchawla@usc.edu) ### 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](https://aclanthology.org/2021.naacl-main.254.pdf)). ### 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](https://aclanthology.org/2021.naacl-main.254.pdf)). #### 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](https://aclanthology.org/2021.naacl-main.254.pdf)). ### Annotations #### Annotation process From the [source paper](https://aclanthology.org/2021.naacl-main.254.pdf) 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](https://aclanthology.org/2021.naacl-main.254.pdf). ### Discussion of Biases Please refer to Section 8.2 in the [source paper](https://aclanthology.org/2021.naacl-main.254.pdf). ### Other Known Limitations Please refer to Section 7 in the [source paper](https://aclanthology.org/2021.naacl-main.254.pdf). ## Additional Information ### Dataset Curators Corresponding Author: Kushal Chawla (`kchawla@usc.edu`)\ Affiliation: University of Southern California\ Please refer to the [source paper](https://aclanthology.org/2021.naacl-main.254.pdf) for the complete author list. ### Licensing Information The project is licensed under CC-by-4.0 ### Citation Information ``` @inproceedings{chawla2021casino, title={CaSiNo: A Corpus of Campsite Negotiation Dialogues for Automatic Negotiation Systems}, author={Chawla, Kushal and Ramirez, Jaysa and Clever, Rene and Lucas, Gale and May, Jonathan and Gratch, Jonathan}, booktitle={Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies}, pages={3167--3185}, year={2021} } ``` ### Contributions Thanks to [Kushal Chawla](https://kushalchawla.github.io/) for adding this dataset.
数据集概述
数据集名称
- 名称: Campsite Negotiation Dialogues
- 别名: CaSiNo
数据集基本信息
- 语言: 英语
- 许可证: CC-BY-4.0
- 多语言性: 单语种
- 数据集大小: 1K<n<10K
- 数据来源: 原始数据
任务类型
- 任务类别: 对话建模、文本生成、填充掩码
- 任务ID: dialogue-modeling
数据集结构
数据实例
- chat_logs: 包含对话文本、任务数据和参与者ID。
- participant_info: 包含参与者的详细信息，如价值观、理由、结果、人口统计和个性特征。
- annotations: 包含对话中每个话语的策略注释。
数据字段
- chat_logs
- text: 对话文本，数据类型为字符串。
- task_data: 包含数据和交易细节，数据类型为字符串。
- id: 参与者ID，数据类型为字符串。
- participant_info
- mturk_agent_1 和 mturk_agent_2: 包含价值观、理由、结果、人口统计和个性特征的详细结构。
- annotations: 包含对话中每个话语的策略注释，数据类型为字符串。
数据分割
- 训练集: 包含1030个对话实例，总字节数为3211407。
数据集创建
数据收集
- 对话收集: 通过Amazon Mechanical Turk进行众包。
- 注释过程: 由三位专家注释者独立完成，共注释了396个对话。
语言创建者
- 语言创建者: 众包工作者。
注释者
- 注释者: 三位专家注释者。
个人和敏感信息
- 信息处理: 所有个人可识别信息在数据发布前已被移除。
