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Dataset Card for openpi_v2
Dataset Summary
Open PI is the first dataset for tracking state changes in procedural text from arbitrary domains by using an unrestricted (open) vocabulary. Our solution is a new task formulation in which just the text is provided, from which a set of state changes (entity, attribute, before, after) is generated for each step, where the entity, attribute, and values must all be predicted from an open vocabulary.
Supported Tasks and Leaderboards
Task 1: Given paragraph (e.g., with 5 steps), identify entities that change (challenge: implicit entities, some explicit entities that don’t change)Task 3: Given paragraph, identify the attributes of entity that change (challenge: implicit entities, attributes & many combinations)Task 4: Given paragraph & an entity, identify the sequence of attribute value changes (challenge: implicit attributes)Task 7: Given image url, identify the visual attributes of entity and non-visual attributes of entity that change
Languages
English
Dataset Structure
Data Instances
A typical instance in the dataset:
{
"goal": "goal1_text",
"steps": [
"step1_text",
"step2_text",
...
],
"topics": "topic1_annotation",
"image_urls": [
"step1_url_text",
"step2_url_text",
...
],
"states": [
{
"answers_openpiv1_metadata": {
"entity": "entity1 | entity2 | ...",
"attribute": "attribute1 | attribute2 | ...",
"answers": [
"before: step1_entity1_before | step1_entity2_before, after: step1_entity1_after | step1_entity2_after",
...
],
"modality": [
"step1_entity1_modality_id | step1_entity2_modality_id",
...
]
},
"entity": "entity1 | entity2 | ...",
"attribute": "attribute1 | attribute2 | ...",
"answers": [
"before: step1_entity1_before_merged | step1_entity2_before_merged, after: step1_entity1_after_merged | step1_entity2_after_merged",
...
]
}
]
}
Data Fields
The following is an excerpt from the dataset README:
Within "goal", "steps", "topics", and "image_urls", the fields should be self-explanatory. Listed below is an explanation about those within "states":
Fields specific to questions:
Data Splits
Train, Valid, Dev
Dataset Creation
Curation Rationale
[More Information Needed]
Source Data
Initial Data Collection and Normalization
[More Information Needed]
Who are the source language producers?
[More Information Needed]
Annotations
Annotation process
[More Information Needed]
Who are the annotators?
[More Information Needed]
Personal and Sensitive Information
[More Information Needed]
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
[More Information Needed]
Licensing Information
[More Information Needed]
Citation Information
[More Information Needed]
Contributions
Thanks to @github-username for adding this dataset.
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