metadata

annotations_creators:
  - crowdsourced
language_creators:
  - crowdsourced
language:
  - en
license:
  - cc-by-4.0
multilinguality:
  - monolingual
size_categories:
  - 10K<n<100K
source_datasets:
  - extended|other-ROC-stories
task_categories:
  - fill-mask
  - text-generation
paperswithcode_id: glucose
pretty_name: GLUCOSE
tags:
  - commonsense-inference
dataset_info:
  features:
    - name: experiment_id
      dtype: string
    - name: story_id
      dtype: string
    - name: worker_id
      dtype: int64
    - name: worker_ids
      dtype: string
    - name: submission_time_normalized
      dtype: string
    - name: worker_quality_assessment
      dtype: int64
    - name: selected_sentence_index
      dtype: int64
    - name: story
      dtype: string
    - name: selected_sentence
      dtype: string
    - name: number_filled_in
      dtype: int64
    - name: 1_specificNL
      dtype: string
    - name: 1_specificStructured
      dtype: string
    - name: 1_generalNL
      dtype: string
    - name: 1_generalStructured
      dtype: string
    - name: 2_specificNL
      dtype: string
    - name: 2_specificStructured
      dtype: string
    - name: 2_generalNL
      dtype: string
    - name: 2_generalStructured
      dtype: string
    - name: 3_specificNL
      dtype: string
    - name: 3_specificStructured
      dtype: string
    - name: 3_generalNL
      dtype: string
    - name: 3_generalStructured
      dtype: string
    - name: 4_specificNL
      dtype: string
    - name: 4_specificStructured
      dtype: string
    - name: 4_generalNL
      dtype: string
    - name: 4_generalStructured
      dtype: string
    - name: 5_specificNL
      dtype: string
    - name: 5_specificStructured
      dtype: string
    - name: 5_generalNL
      dtype: string
    - name: 5_generalStructured
      dtype: string
    - name: 6_specificNL
      dtype: string
    - name: 6_specificStructured
      dtype: string
    - name: 6_generalNL
      dtype: string
    - name: 6_generalStructured
      dtype: string
    - name: 7_specificNL
      dtype: string
    - name: 7_specificStructured
      dtype: string
    - name: 7_generalNL
      dtype: string
    - name: 7_generalStructured
      dtype: string
    - name: 8_specificNL
      dtype: string
    - name: 8_specificStructured
      dtype: string
    - name: 8_generalNL
      dtype: string
    - name: 8_generalStructured
      dtype: string
    - name: 9_specificNL
      dtype: string
    - name: 9_specificStructured
      dtype: string
    - name: 9_generalNL
      dtype: string
    - name: 9_generalStructured
      dtype: string
    - name: 10_specificNL
      dtype: string
    - name: 10_specificStructured
      dtype: string
    - name: 10_generalNL
      dtype: string
    - name: 10_generalStructured
      dtype: string
  config_name: glucose
  splits:
    - name: train
      num_bytes: 204605370
      num_examples: 65522
    - name: test
      num_bytes: 355757
      num_examples: 500
  download_size: 30362105
  dataset_size: 204961127

Dataset Card for [Dataset Name]

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

Dataset Description

Repository
Paper
Point of Contact: glucose@elementalcognition.com

Dataset Summary

GLUCOSE: GeneraLized and COntextualized Story Explanations, is a novel conceptual framework and dataset for commonsense reasoning. Given a short story and a sentence X in the story, GLUCOSE captures ten dimensions of causal explanation related to X. These dimensions, inspired by human cognitive psychology, cover often-implicit causes and effects of X, including events, location, possession, and other attributes.

Supported Tasks and Leaderboards

Common sense inference of:

Causes
Emotions motivating an event
Locations enabling an event
Possession states enabling an event
Other attributes enabling an event
Consequences
Emotions caused by an event
Changes in location caused by an event
Changes in possession caused by an event
Other attributes that may be changed by an event

Languages

English, monolingual

Dataset Structure

Data Instances

{
  "experiment_id": "e56c7c3e-4660-40fb-80d0-052d566d676a__4",
  "story_id": "e56c7c3e-4660-40fb-80d0-052d566d676a",
  "worker_id": 19,
  "submission_time_normalized": "20190930",
  "worker_quality_rating": 3,
  "selected_sentence_index": 4,
  "story": "It was bedtime at our house. Two of the three kids hit the pillow and fall asleep. The third is a trouble maker. For two hours he continues to get out of bed and want to play. Finally he becomes tired and falls asleep."
  selected_sentence: "Finally he becomes tired and falls asleep.",
  "1_specificNL": "The third kid continues to  get out of bed and wants to play >Causes/Enables> The kid finally becomes tired and falls asleep",
  "1_specificStructured": "{The third kid}_[subject] {continues}_[verb] {to }_[preposition1] {get out of bed}_[object1] {and wants to play}_[object2] >Causes/Enables> {The kid}_[subject] {finally becomes}_[verb] {tired}_[object1] {and falls asleep}_[object2]",
  "1_generalNL": "Someone_A doesn't want to  go to sleep >Causes/Enables> Someone_A finally falls asleep",
  "1_generalStructured": "{Someone_A}_[subject] {doesn't want}_[verb] {to }_[preposition1] {go to sleep}_[object1] >Causes/Enables> {Someone_A}_[subject] {finally falls}_[verb] {asleep}_[object1]",
  "2_specificNL": "escaped",
  "2_specificStructured": "escaped",
  "2_generalNL": "escaped",
  "2_generalStructured": "escaped",
  "3_specificNL": "The third kid is in bed >Enables> The kid finally becomes tired and falls asleep",
  "3_specificStructured": "{The third kid}_[subject] {is}_[verb] {in}_[preposition] {bed}_[object] >Enables> {The kid}_[subject] {finally becomes}_[verb] {tired}_[object1] {and falls asleep}_[object2]",
  "3_generalNL": "Someone_A is in bed >Enables> Someone_A falls asleep",
  "3_generalStructured": "{Someone_A}_[subject] {is}_[verb] {in}_[preposition] {bed}_[object] >Enables> {Someone_A}_[subject] {falls}_[verb] {asleep}_[object1]",
  "4_specificNL": "escaped",
  "4_specificStructured": "escaped",
  "4_generalNL": "escaped",
  "4_generalStructured": "escaped",
  "5_specificNL": "escaped",
  "5_specificStructured": "escaped",
  "5_generalNL": "escaped",
  "5_generalStructured": "escaped",
  "6_specificNL": "escaped",
  "6_specificStructured": "escaped",
  "6_generalNL": "escaped",
  "6_generalStructured": "escaped",
  "7_specificNL": "escaped",
  "7_specificStructured": "escaped",
  "7_generalNL": "escaped",
  "7_generalStructured": "escaped",
  "8_specificNL": "escaped",
  "8_specificStructured": "escaped",
  "8_generalNL": "escaped",
  "8_generalStructured": "escaped",
  "9_specificNL": "escaped",
  "9_specificStructured": "escaped",
  "9_generalNL": "escaped",
  "9_generalStructured": "escaped",
  "10_specificNL": "escaped",
  "10_specificStructured": "escaped",
  "10_generalNL": "escaped",
  "10_generalStructured": "escaped",
  "number_filled_in": 7
}

Data Fields

experiment_id: a randomly generated alphanumeric sequence for a given story with the sentence index appended at the end after two underscores. Example: cbee2b5a-f2f9-4bca-9630-6825b1e36c13__0
story_id: a random alphanumeric identifier for the story. Example: e56c7c3e-4660-40fb-80d0-052d566d676a
worker_id: each worker has a unique identificaiton number. Example: 21
submission_time_normalized: the time of submission in the format YYYYMMDD. Example: 20200115
worker_quality_assessment: rating for the worker on the assignment in the row. Example: 2
selected_sentence_index: the index of a given sentence in a story. Example: 0
story: contains the full text of the ROC story that was used for the HIT. Example: It was bedtime at our house. Two of the three kids hit the pillow and fall asleep. The third is a trouble maker. For two hours he continues to get out of bed and want to play. Finally he becomes tired and falls asleep.
selected_sentence: the sentence from the story that is being annotated. Example: It was bedtime at our house.
[1-10]_[specific/general][NL/Structured]: This is the primary data collected. It provides the common sense knowledge about the related stories and those general rules about the world derived from the specific statements. For each of the ten relationships, there are four columns. The specific columns give the specific statements from the story. The general statements give the corresponding generalization. The NL columns are formatted in natural language, whereas the structured columns contain indications of the slots used to fill in the data. Example:
- 1_specificNL: "The school has a football team >Causes/Enables> The football game was last weekend"
- 1_specificStructured: "{The school }_[subject] {has }_[verb] {a football team }_[object1] >Causes/Enables> {The football game }_[subject] {was last weekend }_[verb]"
- 1_generalNL: "Somewhere_A (that is a school ) has Something_A (that is a sports team ) >Causes/Enables> The game was last weekend"
- 1_generalStructured: "{Somewhere_A ||that is a school ||}_[subject] {has }_[verb] {Something_A ||that is a sports team ||}_[object1] >Causes/Enables> {The game }_[subject] {was last weekend }_[verb]"
number_filled_in: number of dimensions filled in for the assignment. Example: 4

Data Splits

Train split: 65,521 examples Test splits: 500 examples, without worker id and rating, number filled in, and structured text.

Dataset Creation

Curation Rationale

When humans read or listen, they make implicit commonsense inferences that frame their understanding of what happened and why. As a step toward AI systems that can build similar mental models, we introduce GLUCOSE, a large-scale dataset of implicit commonsense causal knowledge, encoded as causal mini-theories about the world, each grounded in a narrative context.

Source Data

Initial Data Collection and Normalization

Initial text from ROCStories

Who are the source language producers?

Amazon Mechanical Turk.

Annotations

Annotation process

To enable developing models that can build mental models of narratives, we aimed to crowdsource a large, quality-monitored dataset. Beyond the scalability benefits, using crowd workers (as opposed to a small set of expert annotators) ensures diversity of thought, thus broadening coverage of a common-sense knowledge resource. The annotation task is complex: it requires annotators to understand different causal dimensions in a variety of contexts and to come up with generalized theories beyond the story context. For strict quality control, we designed a three-stage knowledge acquisition pipeline for crowdsourcing the GLUCOSE dataset on the Amazon Mechanical Turk Platform. The workers first go through a qualification test where they must score at least 90% on 10 multiple-choice questions on select GLUCOSE dimensions. Next, qualified workers can work on the main GLUCOSE data collection task: given a story S and a story sentence X, they are asked to fill in (allowing for non-applicable) all ten GLUCOSE dimensions, getting step-by-step guidance from the GLUCOSE data acquisition. To ensure data consistency, the same workers answer all dimensions for an S, X pair. Finally, the submissions are reviewed by an expert who rates each worker on a scale from 0 to 3, and provides feedback on how to improve. Our final UIs are the result of more than six rounds of pilot studies, iteratively improving the interaction elements, functionality, dimension definitions, instructions, and examples.

Who are the annotators?

Amazon Mechanical Turk workers, with feedback from an expert.

Personal and Sensitive Information

No personal or sensitive information.

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

Nasrin Mostafazadeh, Aditya Kalyanpur, Lori Moon, David Buchanan, Lauren Berkowitz, Or Biran, Jennifer Chu-Carroll, from Elemental Cognition

Licensing Information

Creative Commons Attribution-NonCommercial 4.0 International Public License

Citation Information

@inproceedings{mostafazadeh2020glucose,
      title={GLUCOSE: GeneraLized and COntextualized Story Explanations}, 
      author={Nasrin Mostafazadeh and Aditya Kalyanpur and Lori Moon and David Buchanan and Lauren Berkowitz and Or Biran and Jennifer Chu-Carroll},
      year={2020},
      booktitle={The Conference on Empirical Methods in Natural Language Processing},
      publisher={Association for Computational Linguistics}
}

Contributions

Thanks to @TevenLeScao for adding this dataset.

Datasets:
glucose

Dataset Card for [Dataset Name]

Table of Contents

Dataset Description

Dataset Summary

Supported Tasks and Leaderboards

Languages

Dataset Structure

Data Instances

Data Fields

Data Splits

Dataset Creation

Curation Rationale

Source Data

Initial Data Collection and Normalization

Who are the source language producers?

Annotations

Annotation process

Who are the annotators?

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

Contributions