
# Dataset Card for Moral Stories

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-instances)
  - [Data Splits](#data-instances)
- [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)

## Dataset Description

- **Homepage:** [Moral Stories repository](https://github.com/demelin/moral_stories)
- **Repository:** [Moral Stories repository](https://github.com/demelin/moral_stories)
- **Paper:** [Moral Stories: Situated Reasoning about Norms, Intents, Actions, and their Consequences](https://aclanthology.org/2021.emnlp-main.54/)
- **Leaderboard:** [N/A]
- **Point of Contact:** [Denis Emelin](demelin.github.io)

### Dataset Summary

Moral Stories is a crowd-sourced dataset of structured narratives that describe normative and norm-divergent actions taken by individuals to accomplish certain intentions in concrete situations, and their respective consequences. All stories in the dataset consist of seven sentences, belonging to the following categories:  
- Norm: A guideline for social conduct generally observed by most people in everyday situations.
- Situation: Setting of the story that introduces story participants and describes their environment.
- Intention: Reasonable goal that one of the story participants (the actor), wants to fulfill.
- Normative action: An action by the actor that fulfills the intention and observes the norm.
- Normative consequence: Possible effect of the normative action on the actor's environment.
- Divergent action: An action by the actor that fulfills the intention and diverges from the norm.
- Divergent consequence: Possible effect of the divergent action on the actor's environment.  

Accordingly, each story's constituent sentences can be grouped into three segments. The context segment grounds actions within a particular social scenario, the normative path contains the normative action and its consequence, whereas the divergent path includes their norm-divergent analogues. Combining the context segment separately with each path yields two self-contained sub-stories differing in the adherence of the described events to social expectations. See also [*Section 2* in the dataset paper](https://aclanthology.org/2021.emnlp-main.54.pdf).

### Supported Tasks and Leaderboards

- commonsense-reasoning / social-reasoning / moral-reasoning: The dataset can also be used evaluate whether pretrained language models can reason about actions that are either consistent or inconsistent with social norms, the consequences of actions, and the norms that may motivate specific action choices. A [BART model](https://huggingface.co/facebook/bart-large) can be used for this purpose.
- text-classification: This dataset can be used to train models to differentiate between normative and divergent actions as well as between plausible and implausible consequences of actions. A [RoBERTa-based model](https://huggingface.co/roberta-base) can be used for this purpose.
- text-generation: The dataset can be used to train models to generate normative / divergent action hypotheses and their consequences, or norms that may explain certain action choices, conditioned on contexts of different scope. A [GPT-based model](https://huggingface.co/EleutherAI/gpt-neo-2.7B) can be used for this purpose.

### Languages

*Moral Stories* is available in English, with mainstream US Englishes being the dominant variety, as indicated by self-reported contributor demographics provided in the [*Ethical Considerations* section of the dataset paper](https://aclanthology.org/2021.emnlp-main.54.pdf).

## Dataset Structure

### Data Instances

Due to its highly structured nature, *Moral Stories* enables a variety of tasks, such as action classification or norm generation. Furthermore, we provide different data splits in an attempt to challenge generalization abilities of the evaluated models. For details, refer to [*Section 2* in the dataset paper](https://aclanthology.org/2021.emnlp-main.54.pdf). Here, we replicate one instance from the full, task-agnostic dataset, one instance from a classification task dataset (cls-action+norm), and one instance from a generation task dataset (gen-consequence$action+context):  

"full" instance:  
{"ID": "3OJSZ2ATDTUMG7NGR28931AHDQF57F",  
"norm": "It's kind to wait for people.",  
"situation": "Josh drives Penelope to work every day and is hanging outside her house to pick her up, but there's no sign of her.",  
"intention": "Josh needs to get to work.",  
"moral_action": "Josh continues to sit outside Penelope's house until she walks out the door a few minutes later.",  
"moral_consequence": "Penelope appreciates Josh's patience and thanks him for waiting around.",  
"immoral_action": "Josh shrugs his shoulders and drives away to work after sitting around a moment.",  
"immoral_consequence": "Penelope comes running out of the house shouting to wait, but she's too late."}  
  
positive and negative "cls-action+norm" instances (i.e. classify an action as normative or divergent given the action and a relevant norm):  
{"ID": "3EF8EXOTT2TGCKNZROVU3129RYNJ1G1",  
"norm": "It's bad to be too loud and wake people up.",  
"moral_action": "Jordan quietly makes himself a bowl of cereal for breakfast.",  
"label": "1"}  
{"ID": "3EF8EXOTT2TGCKNZROVU3129RYNJ1G0",  
"norm": "It's bad to be too loud and wake people up.",  
"immoral_action": "Jordan noisily bangs a lot of pots and pans when making his breakfast.",  
"label": "0"}  

"gen-consequence$action+context" instance (i.e. generate either a plausible or implausible consequence given an action and its context):  
{"ID": "33FOTY3KENJT46HTHWE8GXPE3LE1C03",  
"norm": "It's mean to hit a bear with a guitar.",  
"situation": "Joe was camping in Montana with his band when he came across a black bear.",  
"intention": "Joe wanted to scare away the black bear.",  
"immoral_action": "Joe grabbed his guitarist's guitar and hit the bear with it.",  
"immoral_consequence": "The bear suffers a concussion and doesn't understand why he was hit.",  
"label": "1"}

### Data Fields

- "ID": Unique identifier ID for this dataset instance.
- "norm": A guideline for social conduct generally observed by most people in everyday situations.
- "situation": Setting of the story that introduces story participants and describes their environment.
- "intention": Reasonable goal that one of the story participants (the actor), wants to fulfill.
- "moral_(i.e. 'normative')_action": An action by the actor that fulfills the intention and observes the norm.
- "moral_consequence": Possible effect of the normative action on the actor's environment.
- "immoral_(i.e. 'divergent')_action": An action by the actor that fulfills the intention and diverges from the norm.
- "immoral_consequence": Possible effect of the divergent action on the actor's environment.  
- "label": Data instance label; for action-related tasks, "0" corresponds to an immoral / divergent action while "1" corresponds to a moral / normative action, for consequence-related tasks, "0" corresponds to a plausible consequence while "1" corresponds to an implausible consequence (for generation tasks, label is always set to "1")

### Data Splits

For classification tasks, we examined three data split strategies:

- *Norm Distance*: Norms are based on social consensus and may, as such, change across time and between locations. Therefore, we are also interested in how well classification models can generalize to novel norms. To estimate this, we split the dataset by embedding
norms found in the collected stories and grouping them into 1k clusters via agglomerative clustering. Clusters are ordered according to their degree of isolation, defined as the cosine distance between a cluster's centroid and the next-closest cluster's centroid. Stories with norms from most isolated clusters are assigned to test and development sets, with the rest forming the training set. 
- *Lexical Bias*: Tests the susceptibility of classifiers to surface-level lexical correlations. We first identify 100 biased lemmas that occur most frequently either in normative or divergent actions. Each story is then assigned a bias score corresponding to the total number of biased lemmas present in both actions (or consequences). Starting with the lowest bias scores, stories are assigned to the test, development, and, lastly, training set.
- *Minimal Pairs*: Evaluates the model's ability to perform nuanced social reasoning. Splits are obtained by ordering stories according to the Damerau-Levenshtein distance between their actions (or consequences) and assigning stories with lowest distances to the test set, followed by the development set. The remainder makes up the training set.  
  
For generation tasks, only the *Norm Distance* split strategy is used. For more details, refer to  [*Section 3* and *Appendix C* in the dataset paper](https://aclanthology.org/2021.emnlp-main.54.pdf). 

## Dataset Creation

### Curation Rationale

Please refer to [*Section 2* and the *Ethical Considerations* section in the dataset paper](https://aclanthology.org/2021.emnlp-main.54.pdf).

### Source Data

#### Initial Data Collection and Normalization

Please refer to [*Section 2* in the dataset paper](https://aclanthology.org/2021.emnlp-main.54.pdf).

#### Who are the source language producers?

Please refer to [the *Ethical Considerations* section in the dataset paper](https://aclanthology.org/2021.emnlp-main.54.pdf).

### Annotations

#### Annotation process

Please refer to [*Section 2* and the *Ethical Considerations* section in the dataset paper](https://aclanthology.org/2021.emnlp-main.54.pdf).

#### Who are the annotators?

Please refer to [the *Ethical Considerations* section in the dataset paper](https://aclanthology.org/2021.emnlp-main.54.pdf).

### Personal and Sensitive Information

[N/A]

## Considerations for Using the Data

### Social Impact of Dataset

Please refer to [the *Ethical Considerations* section in the dataset paper](https://aclanthology.org/2021.emnlp-main.54.pdf).

### Discussion of Biases

Please refer to [the *Ethical Considerations* section in the dataset paper](https://aclanthology.org/2021.emnlp-main.54.pdf).

### Other Known Limitations

Please refer to [the *Ethical Considerations* section in the dataset paper](https://aclanthology.org/2021.emnlp-main.54.pdf).

## Additional Information

### Dataset Curators

[Denis Emelin](demelin.github.io) 

### Licensing Information

MIT

### Citation Information

@article{Emelin2021MoralSS,
  title={Moral Stories: Situated Reasoning about Norms, Intents, Actions, and their Consequences},
  author={Denis Emelin and Ronan Le Bras and Jena D. Hwang and Maxwell Forbes and Yejin Choi},
  journal={ArXiv},
  year={2021},
  volume={abs/2012.15738}
}
task_categories:multiple-choice task_categories:text-generation task_categories:text-classification task_ids:multiple-choice-qa task_ids:language-modeling task_ids:text-scoring annotations_creators:no-annotation language_creators:crowdsourced multilinguality:monolingual size_categories:10K<n<100K source_datasets:original language:en license:mit region:us
{"annotations_creators": ["no-annotation"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["multiple-choice", "text-generation", "text-classification", "commonsense-reasoning", "moral-reasoning", "social-reasoning"], "task_ids": ["multiple-choice-qa", "language-modeling", "text-scoring"], "pretty_name": "Moral Stories"}
TAGS
#task_categories-multiple-choice #task_categories-text-generation #task_categories-text-classification #task_ids-multiple-choice-qa #task_ids-language-modeling #task_ids-text-scoring #annotations_creators-no-annotation #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-mit #region-us 


# Dataset Card for Moral Stories

## Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - Personal and Sensitive Information
- Considerations for Using the Data
  - Social Impact of Dataset
  - Discussion of Biases
  - Other Known Limitations
- Additional Information
  - Dataset Curators
  - Licensing Information
  - Citation Information

## Dataset Description

- Homepage: Moral Stories repository
- Repository: Moral Stories repository
- Paper: Moral Stories: Situated Reasoning about Norms, Intents, Actions, and their Consequences
- Leaderboard: [N/A]
- Point of Contact: Denis Emelin

### Dataset Summary

Moral Stories is a crowd-sourced dataset of structured narratives that describe normative and norm-divergent actions taken by individuals to accomplish certain intentions in concrete situations, and their respective consequences. All stories in the dataset consist of seven sentences, belonging to the following categories:  
- Norm: A guideline for social conduct generally observed by most people in everyday situations.
- Situation: Setting of the story that introduces story participants and describes their environment.
- Intention: Reasonable goal that one of the story participants (the actor), wants to fulfill.
- Normative action: An action by the actor that fulfills the intention and observes the norm.
- Normative consequence: Possible effect of the normative action on the actor's environment.
- Divergent action: An action by the actor that fulfills the intention and diverges from the norm.
- Divergent consequence: Possible effect of the divergent action on the actor's environment.  

Accordingly, each story's constituent sentences can be grouped into three segments. The context segment grounds actions within a particular social scenario, the normative path contains the normative action and its consequence, whereas the divergent path includes their norm-divergent analogues. Combining the context segment separately with each path yields two self-contained sub-stories differing in the adherence of the described events to social expectations. See also *Section 2* in the dataset paper.

### Supported Tasks and Leaderboards

- commonsense-reasoning / social-reasoning / moral-reasoning: The dataset can also be used evaluate whether pretrained language models can reason about actions that are either consistent or inconsistent with social norms, the consequences of actions, and the norms that may motivate specific action choices. A BART model can be used for this purpose.
- text-classification: This dataset can be used to train models to differentiate between normative and divergent actions as well as between plausible and implausible consequences of actions. A RoBERTa-based model can be used for this purpose.
- text-generation: The dataset can be used to train models to generate normative / divergent action hypotheses and their consequences, or norms that may explain certain action choices, conditioned on contexts of different scope. A GPT-based model can be used for this purpose.

### Languages

*Moral Stories* is available in English, with mainstream US Englishes being the dominant variety, as indicated by self-reported contributor demographics provided in the *Ethical Considerations* section of the dataset paper.

## Dataset Structure

### Data Instances

Due to its highly structured nature, *Moral Stories* enables a variety of tasks, such as action classification or norm generation. Furthermore, we provide different data splits in an attempt to challenge generalization abilities of the evaluated models. For details, refer to *Section 2* in the dataset paper. Here, we replicate one instance from the full, task-agnostic dataset, one instance from a classification task dataset (cls-action+norm), and one instance from a generation task dataset (gen-consequence$action+context):  

"full" instance:  
{"ID": "3OJSZ2ATDTUMG7NGR28931AHDQF57F",  
"norm": "It's kind to wait for people.",  
"situation": "Josh drives Penelope to work every day and is hanging outside her house to pick her up, but there's no sign of her.",  
"intention": "Josh needs to get to work.",  
"moral_action": "Josh continues to sit outside Penelope's house until she walks out the door a few minutes later.",  
"moral_consequence": "Penelope appreciates Josh's patience and thanks him for waiting around.",  
"immoral_action": "Josh shrugs his shoulders and drives away to work after sitting around a moment.",  
"immoral_consequence": "Penelope comes running out of the house shouting to wait, but she's too late."}  
  
positive and negative "cls-action+norm" instances (i.e. classify an action as normative or divergent given the action and a relevant norm):  
{"ID": "3EF8EXOTT2TGCKNZROVU3129RYNJ1G1",  
"norm": "It's bad to be too loud and wake people up.",  
"moral_action": "Jordan quietly makes himself a bowl of cereal for breakfast.",  
"label": "1"}  
{"ID": "3EF8EXOTT2TGCKNZROVU3129RYNJ1G0",  
"norm": "It's bad to be too loud and wake people up.",  
"immoral_action": "Jordan noisily bangs a lot of pots and pans when making his breakfast.",  
"label": "0"}  

"gen-consequence$action+context" instance (i.e. generate either a plausible or implausible consequence given an action and its context):  
{"ID": "33FOTY3KENJT46HTHWE8GXPE3LE1C03",  
"norm": "It's mean to hit a bear with a guitar.",  
"situation": "Joe was camping in Montana with his band when he came across a black bear.",  
"intention": "Joe wanted to scare away the black bear.",  
"immoral_action": "Joe grabbed his guitarist's guitar and hit the bear with it.",  
"immoral_consequence": "The bear suffers a concussion and doesn't understand why he was hit.",  
"label": "1"}

### Data Fields

- "ID": Unique identifier ID for this dataset instance.
- "norm": A guideline for social conduct generally observed by most people in everyday situations.
- "situation": Setting of the story that introduces story participants and describes their environment.
- "intention": Reasonable goal that one of the story participants (the actor), wants to fulfill.
- "moral_(i.e. 'normative')_action": An action by the actor that fulfills the intention and observes the norm.
- "moral_consequence": Possible effect of the normative action on the actor's environment.
- "immoral_(i.e. 'divergent')_action": An action by the actor that fulfills the intention and diverges from the norm.
- "immoral_consequence": Possible effect of the divergent action on the actor's environment.  
- "label": Data instance label; for action-related tasks, "0" corresponds to an immoral / divergent action while "1" corresponds to a moral / normative action, for consequence-related tasks, "0" corresponds to a plausible consequence while "1" corresponds to an implausible consequence (for generation tasks, label is always set to "1")

### Data Splits

For classification tasks, we examined three data split strategies:

- *Norm Distance*: Norms are based on social consensus and may, as such, change across time and between locations. Therefore, we are also interested in how well classification models can generalize to novel norms. To estimate this, we split the dataset by embedding
norms found in the collected stories and grouping them into 1k clusters via agglomerative clustering. Clusters are ordered according to their degree of isolation, defined as the cosine distance between a cluster's centroid and the next-closest cluster's centroid. Stories with norms from most isolated clusters are assigned to test and development sets, with the rest forming the training set. 
- *Lexical Bias*: Tests the susceptibility of classifiers to surface-level lexical correlations. We first identify 100 biased lemmas that occur most frequently either in normative or divergent actions. Each story is then assigned a bias score corresponding to the total number of biased lemmas present in both actions (or consequences). Starting with the lowest bias scores, stories are assigned to the test, development, and, lastly, training set.
- *Minimal Pairs*: Evaluates the model's ability to perform nuanced social reasoning. Splits are obtained by ordering stories according to the Damerau-Levenshtein distance between their actions (or consequences) and assigning stories with lowest distances to the test set, followed by the development set. The remainder makes up the training set.  
  
For generation tasks, only the *Norm Distance* split strategy is used. For more details, refer to  *Section 3* and *Appendix C* in the dataset paper. 

## Dataset Creation

### Curation Rationale

Please refer to *Section 2* and the *Ethical Considerations* section in the dataset paper.

### Source Data

#### Initial Data Collection and Normalization

Please refer to *Section 2* in the dataset paper.

#### Who are the source language producers?

Please refer to the *Ethical Considerations* section in the dataset paper.

### Annotations

#### Annotation process

Please refer to *Section 2* and the *Ethical Considerations* section in the dataset paper.

#### Who are the annotators?

Please refer to the *Ethical Considerations* section in the dataset paper.

### Personal and Sensitive Information

[N/A]

## Considerations for Using the Data

### Social Impact of Dataset

Please refer to the *Ethical Considerations* section in the dataset paper.

### Discussion of Biases

Please refer to the *Ethical Considerations* section in the dataset paper.

### Other Known Limitations

Please refer to the *Ethical Considerations* section in the dataset paper.

## Additional Information

### Dataset Curators

Denis Emelin 

### Licensing Information

MIT



@article{Emelin2021MoralSS,
  title={Moral Stories: Situated Reasoning about Norms, Intents, Actions, and their Consequences},
  author={Denis Emelin and Ronan Le Bras and Jena D. Hwang and Maxwell Forbes and Yejin Choi},
  journal={ArXiv},
  year={2021},
  volume={abs/2012.15738}
}
# Dataset Card for Moral Stories ## Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - Personal and Sensitive Information
- Considerations for Using the Data
  - Social Impact of Dataset
  - Discussion of Biases
  - Other Known Limitations
- Additional Information
  - Dataset Curators
  - Licensing Information
  - Citation Information ## Dataset Description

- Homepage: Moral Stories repository
- Repository: Moral Stories repository
- Paper: Moral Stories: Situated Reasoning about Norms, Intents, Actions, and their Consequences
- Leaderboard: [N/A]
- Point of Contact: Denis Emelin ### Dataset Summary

Moral Stories is a crowd-sourced dataset of structured narratives that describe normative and norm-divergent actions taken by individuals to accomplish certain intentions in concrete situations, and their respective consequences. All stories in the dataset consist of seven sentences, belonging to the following categories:  
- Norm: A guideline for social conduct generally observed by most people in everyday situations.
- Situation: Setting of the story that introduces story participants and describes their environment.
- Intention: Reasonable goal that one of the story participants (the actor), wants to fulfill.
- Normative action: An action by the actor that fulfills the intention and observes the norm.
- Normative consequence: Possible effect of the normative action on the actor's environment.
- Divergent action: An action by the actor that fulfills the intention and diverges from the norm.
- Divergent consequence: Possible effect of the divergent action on the actor's environment.  

Accordingly, each story's constituent sentences can be grouped into three segments. The context segment grounds actions within a particular social scenario, the normative path contains the normative action and its consequence, whereas the divergent path includes their norm-divergent analogues. Combining the context segment separately with each path yields two self-contained sub-stories differing in the adherence of the described events to social expectations. See also *Section 2* in the dataset paper. ### Supported Tasks and Leaderboards

- commonsense-reasoning / social-reasoning / moral-reasoning: The dataset can also be used evaluate whether pretrained language models can reason about actions that are either consistent or inconsistent with social norms, the consequences of actions, and the norms that may motivate specific action choices. A BART model can be used for this purpose.
- text-classification: This dataset can be used to train models to differentiate between normative and divergent actions as well as between plausible and implausible consequences of actions. A RoBERTa-based model can be used for this purpose.
- text-generation: The dataset can be used to train models to generate normative / divergent action hypotheses and their consequences, or norms that may explain certain action choices, conditioned on contexts of different scope. A GPT-based model can be used for this purpose. ### Languages

*Moral Stories* is available in English, with mainstream US Englishes being the dominant variety, as indicated by self-reported contributor demographics provided in the *Ethical Considerations* section of the dataset paper. ## Dataset Structure ### Data Instances

Due to its highly structured nature, *Moral Stories* enables a variety of tasks, such as action classification or norm generation. Furthermore, we provide different data splits in an attempt to challenge generalization abilities of the evaluated models. For details, refer to *Section 2* in the dataset paper. Here, we replicate one instance from the full, task-agnostic dataset, one instance from a classification task dataset (cls-action+norm), and one instance from a generation task dataset (gen-consequence$action+context):  

"full" instance:  
{"ID": "3OJSZ2ATDTUMG7NGR28931AHDQF57F",  
"norm": "It's kind to wait for people.",  
"situation": "Josh drives Penelope to work every day and is hanging outside her house to pick her up, but there's no sign of her.",  
"intention": "Josh needs to get to work.",  
"moral_action": "Josh continues to sit outside Penelope's house until she walks out the door a few minutes later.",  
"moral_consequence": "Penelope appreciates Josh's patience and thanks him for waiting around.",  
"immoral_action": "Josh shrugs his shoulders and drives away to work after sitting around a moment.",  
"immoral_consequence": "Penelope comes running out of the house shouting to wait, but she's too late."}  
  
positive and negative "cls-action+norm" instances (i.e. classify an action as normative or divergent given the action and a relevant norm):  
{"ID": "3EF8EXOTT2TGCKNZROVU3129RYNJ1G1",  
"norm": "It's bad to be too loud and wake people up.",  
"moral_action": "Jordan quietly makes himself a bowl of cereal for breakfast.",  
"label": "1"}  
{"ID": "3EF8EXOTT2TGCKNZROVU3129RYNJ1G0",  
"norm": "It's bad to be too loud and wake people up.",  
"immoral_action": "Jordan noisily bangs a lot of pots and pans when making his breakfast.",  
"label": "0"}  

"gen-consequence$action+context" instance (i.e. generate either a plausible or implausible consequence given an action and its context):  
{"ID": "33FOTY3KENJT46HTHWE8GXPE3LE1C03",  
"norm": "It's mean to hit a bear with a guitar.",  
"situation": "Joe was camping in Montana with his band when he came across a black bear.",  
"intention": "Joe wanted to scare away the black bear.",  
"immoral_action": "Joe grabbed his guitarist's guitar and hit the bear with it.",  
"immoral_consequence": "The bear suffers a concussion and doesn't understand why he was hit.",  
"label": "1"} ### Data Fields

- "ID": Unique identifier ID for this dataset instance.
- "norm": A guideline for social conduct generally observed by most people in everyday situations.
- "situation": Setting of the story that introduces story participants and describes their environment.
- "intention": Reasonable goal that one of the story participants (the actor), wants to fulfill.
- "moral_(i.e. 'normative')_action": An action by the actor that fulfills the intention and observes the norm.
- "moral_consequence": Possible effect of the normative action on the actor's environment.
- "immoral_(i.e. 'divergent')_action": An action by the actor that fulfills the intention and diverges from the norm.
- "immoral_consequence": Possible effect of the divergent action on the actor's environment.  
- "label": Data instance label; for action-related tasks, "0" corresponds to an immoral / divergent action while "1" corresponds to a moral / normative action, for consequence-related tasks, "0" corresponds to a plausible consequence while "1" corresponds to an implausible consequence (for generation tasks, label is always set to "1") ### Data Splits

For classification tasks, we examined three data split strategies:

- *Norm Distance*: Norms are based on social consensus and may, as such, change across time and between locations. Therefore, we are also interested in how well classification models can generalize to novel norms. To estimate this, we split the dataset by embedding
norms found in the collected stories and grouping them into 1k clusters via agglomerative clustering. Clusters are ordered according to their degree of isolation, defined as the cosine distance between a cluster's centroid and the next-closest cluster's centroid. Stories with norms from most isolated clusters are assigned to test and development sets, with the rest forming the training set. 
- *Lexical Bias*: Tests the susceptibility of classifiers to surface-level lexical correlations. We first identify 100 biased lemmas that occur most frequently either in normative or divergent actions. Each story is then assigned a bias score corresponding to the total number of biased lemmas present in both actions (or consequences). Starting with the lowest bias scores, stories are assigned to the test, development, and, lastly, training set.
- *Minimal Pairs*: Evaluates the model's ability to perform nuanced social reasoning. Splits are obtained by ordering stories according to the Damerau-Levenshtein distance between their actions (or consequences) and assigning stories with lowest distances to the test set, followed by the development set. The remainder makes up the training set.  
  
For generation tasks, only the *Norm Distance* split strategy is used. For more details, refer to  *Section 3* and *Appendix C* in the dataset paper. ## Dataset Creation ### Curation Rationale

Please refer to *Section 2* and the *Ethical Considerations* section in the dataset paper. ### Source Data #### Initial Data Collection and Normalization

Please refer to *Section 2* in the dataset paper. #### Who are the source language producers?

Please refer to the *Ethical Considerations* section in the dataset paper. ### Annotations #### Annotation process

Please refer to *Section 2* and the *Ethical Considerations* section in the dataset paper. #### Who are the annotators?

Please refer to the *Ethical Considerations* section in the dataset paper. ### Personal and Sensitive Information

[N/A] ## Considerations for Using the Data ### Social Impact of Dataset

Please refer to the *Ethical Considerations* section in the dataset paper. ### Discussion of Biases

Please refer to the *Ethical Considerations* section in the dataset paper. ### Other Known Limitations

Please refer to the *Ethical Considerations* section in the dataset paper. ## Additional Information ### Dataset Curators

Denis Emelin ### Licensing Information

MIT



@article{Emelin2021MoralSS,
  title={Moral Stories: Situated Reasoning about Norms, Intents, Actions, and their Consequences},
  author={Denis Emelin and Ronan Le Bras and Jena D. Hwang and Maxwell Forbes and Yejin Choi},
  journal={ArXiv},
  year={2021},
  volume={abs/2012.15738}
}
TAGS
#task_categories-multiple-choice #task_categories-text-generation #task_categories-text-classification #task_ids-multiple-choice-qa #task_ids-language-modeling #task_ids-text-scoring #annotations_creators-no-annotation #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-mit #region-us 
 # Dataset Card for Moral Stories ## Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - Personal and Sensitive Information
- Considerations for Using the Data
  - Social Impact of Dataset
  - Discussion of Biases
  - Other Known Limitations
- Additional Information
  - Dataset Curators
  - Licensing Information
  - Citation Information ## Dataset Description

- Homepage: Moral Stories repository
- Repository: Moral Stories repository
- Paper: Moral Stories: Situated Reasoning about Norms, Intents, Actions, and their Consequences
- Leaderboard: [N/A]
- Point of Contact: Denis Emelin ### Dataset Summary

Moral Stories is a crowd-sourced dataset of structured narratives that describe normative and norm-divergent actions taken by individuals to accomplish certain intentions in concrete situations, and their respective consequences. All stories in the dataset consist of seven sentences, belonging to the following categories:  
- Norm: A guideline for social conduct generally observed by most people in everyday situations.
- Situation: Setting of the story that introduces story participants and describes their environment.
- Intention: Reasonable goal that one of the story participants (the actor), wants to fulfill.
- Normative action: An action by the actor that fulfills the intention and observes the norm.
- Normative consequence: Possible effect of the normative action on the actor's environment.
- Divergent action: An action by the actor that fulfills the intention and diverges from the norm.
- Divergent consequence: Possible effect of the divergent action on the actor's environment.  

Accordingly, each story's constituent sentences can be grouped into three segments. The context segment grounds actions within a particular social scenario, the normative path contains the normative action and its consequence, whereas the divergent path includes their norm-divergent analogues. Combining the context segment separately with each path yields two self-contained sub-stories differing in the adherence of the described events to social expectations. See also *Section 2* in the dataset paper. ### Supported Tasks and Leaderboards

- commonsense-reasoning / social-reasoning / moral-reasoning: The dataset can also be used evaluate whether pretrained language models can reason about actions that are either consistent or inconsistent with social norms, the consequences of actions, and the norms that may motivate specific action choices. A BART model can be used for this purpose.
- text-classification: This dataset can be used to train models to differentiate between normative and divergent actions as well as between plausible and implausible consequences of actions. A RoBERTa-based model can be used for this purpose.
- text-generation: The dataset can be used to train models to generate normative / divergent action hypotheses and their consequences, or norms that may explain certain action choices, conditioned on contexts of different scope. A GPT-based model can be used for this purpose. ### Languages

*Moral Stories* is available in English, with mainstream US Englishes being the dominant variety, as indicated by self-reported contributor demographics provided in the *Ethical Considerations* section of the dataset paper. ## Dataset Structure ### Data Instances

Due to its highly structured nature, *Moral Stories* enables a variety of tasks, such as action classification or norm generation. Furthermore, we provide different data splits in an attempt to challenge generalization abilities of the evaluated models. For details, refer to *Section 2* in the dataset paper. Here, we replicate one instance from the full, task-agnostic dataset, one instance from a classification task dataset (cls-action+norm), and one instance from a generation task dataset (gen-consequence$action+context):  

"full" instance:  
{"ID": "3OJSZ2ATDTUMG7NGR28931AHDQF57F",  
"norm": "It's kind to wait for people.",  
"situation": "Josh drives Penelope to work every day and is hanging outside her house to pick her up, but there's no sign of her.",  
"intention": "Josh needs to get to work.",  
"moral_action": "Josh continues to sit outside Penelope's house until she walks out the door a few minutes later.",  
"moral_consequence": "Penelope appreciates Josh's patience and thanks him for waiting around.",  
"immoral_action": "Josh shrugs his shoulders and drives away to work after sitting around a moment.",  
"immoral_consequence": "Penelope comes running out of the house shouting to wait, but she's too late."}  
  
positive and negative "cls-action+norm" instances (i.e. classify an action as normative or divergent given the action and a relevant norm):  
{"ID": "3EF8EXOTT2TGCKNZROVU3129RYNJ1G1",  
"norm": "It's bad to be too loud and wake people up.",  
"moral_action": "Jordan quietly makes himself a bowl of cereal for breakfast.",  
"label": "1"}  
{"ID": "3EF8EXOTT2TGCKNZROVU3129RYNJ1G0",  
"norm": "It's bad to be too loud and wake people up.",  
"immoral_action": "Jordan noisily bangs a lot of pots and pans when making his breakfast.",  
"label": "0"}  

"gen-consequence$action+context" instance (i.e. generate either a plausible or implausible consequence given an action and its context):  
{"ID": "33FOTY3KENJT46HTHWE8GXPE3LE1C03",  
"norm": "It's mean to hit a bear with a guitar.",  
"situation": "Joe was camping in Montana with his band when he came across a black bear.",  
"intention": "Joe wanted to scare away the black bear.",  
"immoral_action": "Joe grabbed his guitarist's guitar and hit the bear with it.",  
"immoral_consequence": "The bear suffers a concussion and doesn't understand why he was hit.",  
"label": "1"} ### Data Fields

- "ID": Unique identifier ID for this dataset instance.
- "norm": A guideline for social conduct generally observed by most people in everyday situations.
- "situation": Setting of the story that introduces story participants and describes their environment.
- "intention": Reasonable goal that one of the story participants (the actor), wants to fulfill.
- "moral_(i.e. 'normative')_action": An action by the actor that fulfills the intention and observes the norm.
- "moral_consequence": Possible effect of the normative action on the actor's environment.
- "immoral_(i.e. 'divergent')_action": An action by the actor that fulfills the intention and diverges from the norm.
- "immoral_consequence": Possible effect of the divergent action on the actor's environment.  
- "label": Data instance label; for action-related tasks, "0" corresponds to an immoral / divergent action while "1" corresponds to a moral / normative action, for consequence-related tasks, "0" corresponds to a plausible consequence while "1" corresponds to an implausible consequence (for generation tasks, label is always set to "1") ### Data Splits

For classification tasks, we examined three data split strategies:

- *Norm Distance*: Norms are based on social consensus and may, as such, change across time and between locations. Therefore, we are also interested in how well classification models can generalize to novel norms. To estimate this, we split the dataset by embedding
norms found in the collected stories and grouping them into 1k clusters via agglomerative clustering. Clusters are ordered according to their degree of isolation, defined as the cosine distance between a cluster's centroid and the next-closest cluster's centroid. Stories with norms from most isolated clusters are assigned to test and development sets, with the rest forming the training set. 
- *Lexical Bias*: Tests the susceptibility of classifiers to surface-level lexical correlations. We first identify 100 biased lemmas that occur most frequently either in normative or divergent actions. Each story is then assigned a bias score corresponding to the total number of biased lemmas present in both actions (or consequences). Starting with the lowest bias scores, stories are assigned to the test, development, and, lastly, training set.
- *Minimal Pairs*: Evaluates the model's ability to perform nuanced social reasoning. Splits are obtained by ordering stories according to the Damerau-Levenshtein distance between their actions (or consequences) and assigning stories with lowest distances to the test set, followed by the development set. The remainder makes up the training set.  
  
For generation tasks, only the *Norm Distance* split strategy is used. For more details, refer to  *Section 3* and *Appendix C* in the dataset paper. ## Dataset Creation ### Curation Rationale

Please refer to *Section 2* and the *Ethical Considerations* section in the dataset paper. ### Source Data #### Initial Data Collection and Normalization

Please refer to *Section 2* in the dataset paper. #### Who are the source language producers?

Please refer to the *Ethical Considerations* section in the dataset paper. ### Annotations #### Annotation process

Please refer to *Section 2* and the *Ethical Considerations* section in the dataset paper. #### Who are the annotators?

Please refer to the *Ethical Considerations* section in the dataset paper. ### Personal and Sensitive Information

[N/A] ## Considerations for Using the Data ### Social Impact of Dataset

Please refer to the *Ethical Considerations* section in the dataset paper. ### Discussion of Biases

Please refer to the *Ethical Considerations* section in the dataset paper. ### Other Known Limitations

Please refer to the *Ethical Considerations* section in the dataset paper. ## Additional Information ### Dataset Curators

Denis Emelin ### Licensing Information

MIT



@article{Emelin2021MoralSS,
  title={Moral Stories: Situated Reasoning about Norms, Intents, Actions, and their Consequences},
  author={Denis Emelin and Ronan Le Bras and Jena D. Hwang and Maxwell Forbes and Yejin Choi},
  journal={ArXiv},
  year={2021},
  volume={abs/2012.15738}
}
passage: TAGS
#task_categories-multiple-choice #task_categories-text-generation #task_categories-text-classification #task_ids-multiple-choice-qa #task_ids-language-modeling #task_ids-text-scoring #annotations_creators-no-annotation #language_creators-crowdsourced #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-mit #region-us 
# Dataset Card for Moral Stories## Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - Personal and Sensitive Information
- Considerations for Using the Data
  - Social Impact of Dataset
  - Discussion of Biases
  - Other Known Limitations
- Additional Information
  - Dataset Curators
  - Licensing Information
  - Citation Information## Dataset Description

- Homepage: Moral Stories repository
- Repository: Moral Stories repository
- Paper: Moral Stories: Situated Reasoning about Norms, Intents, Actions, and their Consequences
- Leaderboard: [N/A]
- Point of Contact: Denis Emelin passage: ### Dataset Summary

Moral Stories is a crowd-sourced dataset of structured narratives that describe normative and norm-divergent actions taken by individuals to accomplish certain intentions in concrete situations, and their respective consequences. All stories in the dataset consist of seven sentences, belonging to the following categories:  
- Norm: A guideline for social conduct generally observed by most people in everyday situations.
- Situation: Setting of the story that introduces story participants and describes their environment.
- Intention: Reasonable goal that one of the story participants (the actor), wants to fulfill.
- Normative action: An action by the actor that fulfills the intention and observes the norm.
- Normative consequence: Possible effect of the normative action on the actor's environment.
- Divergent action: An action by the actor that fulfills the intention and diverges from the norm.
- Divergent consequence: Possible effect of the divergent action on the actor's environment.  

Accordingly, each story's constituent sentences can be grouped into three segments. The context segment grounds actions within a particular social scenario, the normative path contains the normative action and its consequence, whereas the divergent path includes their norm-divergent analogues. Combining the context segment separately with each path yields two self-contained sub-stories differing in the adherence of the described events to social expectations. See also *Section 2* in the dataset paper.### Supported Tasks and Leaderboards

- commonsense-reasoning / social-reasoning / moral-reasoning: The dataset can also be used evaluate whether pretrained language models can reason about actions that are either consistent or inconsistent with social norms, the consequences of actions, and the norms that may motivate specific action choices. A BART model can be used for this purpose.
- text-classification: This dataset can be used to train models to differentiate between normative and divergent actions as well as between plausible and implausible consequences of actions. A RoBERTa-based model can be used for this purpose.
- text-generation: The dataset can be used to train models to generate normative / divergent action hypotheses and their consequences, or norms that may explain certain action choices, conditioned on contexts of different scope. A GPT-based model can be used for this purpose.### Languages

*Moral Stories* is available in English, with mainstream US Englishes being the dominant variety, as indicated by self-reported contributor demographics provided in the *Ethical Considerations* section of the dataset paper.## Dataset Structure passage: ### Data Instances

Due to its highly structured nature, *Moral Stories* enables a variety of tasks, such as action classification or norm generation. Furthermore, we provide different data splits in an attempt to challenge generalization abilities of the evaluated models. For details, refer to *Section 2* in the dataset paper. Here, we replicate one instance from the full, task-agnostic dataset, one instance from a classification task dataset (cls-action+norm), and one instance from a generation task dataset (gen-consequence$action+context):  

"full" instance:  
{"ID": "3OJSZ2ATDTUMG7NGR28931AHDQF57F",  
"norm": "It's kind to wait for people.",  
"situation": "Josh drives Penelope to work every day and is hanging outside her house to pick her up, but there's no sign of her.",  
"intention": "Josh needs to get to work.",  
"moral_action": "Josh continues to sit outside Penelope's house until she walks out the door a few minutes later.",  
"moral_consequence": "Penelope appreciates Josh's patience and thanks him for waiting around.",  
"immoral_action": "Josh shrugs his shoulders and drives away to work after sitting around a moment.",  
"immoral_consequence": "Penelope comes running out of the house shouting to wait, but she's too late."}  
  
positive and negative "cls-action+norm" instances (i.e. classify an action as normative or divergent given the action and a relevant norm):  
{"ID": "3EF8EXOTT2TGCKNZROVU3129RYNJ1G1",  
"norm": "It's bad to be too loud and wake people up.",  
"moral_action": "Jordan quietly makes himself a bowl of cereal for breakfast.",  
"label": "1"}  
{"ID": "3EF8EXOTT2TGCKNZROVU3129RYNJ1G0",  
"norm": "It's bad to be too loud and wake people up.",  
"immoral_action": "Jordan noisily bangs a lot of pots and pans when making his breakfast.",  
"label": "0"}  

"gen-consequence$action+context" instance (i.e. generate either a plausible or implausible consequence given an action and its context):  
{"ID": "33FOTY3KENJT46HTHWE8GXPE3LE1C03",  
"norm": "It's mean to hit a bear with a guitar.",  
"situation": "Joe was camping in Montana with his band when he came across a black bear.",  
"intention": "Joe wanted to scare away the black bear.",  
"immoral_action": "Joe grabbed his guitarist's guitar and hit the bear with it.",  
"immoral_consequence": "The bear suffers a concussion and doesn't understand why he was hit.",  
"label": "1"}### Data Fields

- "ID": Unique identifier ID for this dataset instance.
- "norm": A guideline for social conduct generally observed by most people in everyday situations.
- "situation": Setting of the story that introduces story participants and describes their environment.
- "intention": Reasonable goal that one of the story participants (the actor), wants to fulfill.
- "moral_(i.e. 'normative')_action": An action by the actor that fulfills the intention and observes the norm.
- "moral_consequence": Possible effect of the normative action on the actor's environment.
- "immoral_(i.e. 'divergent')_action": An action by the actor that fulfills the intention and diverges from the norm.
- "immoral_consequence": Possible effect of the divergent action on the actor's environment.  
- "label": Data instance label; for action-related tasks, "0" corresponds to an immoral / divergent action while "1" corresponds to a moral / normative action, for consequence-related tasks, "0" corresponds to a plausible consequence while "1" corresponds to an implausible consequence (for generation tasks, label is always set to "1")