AMAMMERε Overview
The AMAMMERε Dataset delves into the subtle cultural biases present in common sense reasoning datasets, highlighting predominantly Western or U.S.-centric perspectives. By comparing cultural commonsense reasoning between Ghana (representing a low-resource language group) and the USA, this dataset shows the differences in daily activities such as shopping, meal planning, and transportation across these distinct cultural contexts.
Highlights:
525 Questions: Curated from existing commonsense datasets and adapted to reflect Ghanaian and U.S. cultural settings, emphasizing cultural disparities.
Multiple Formats: Available in CSV, Excel, and JSONL formats
Validated Annotations: Reviewed by human annotators from both the USA and Ghana to ensure accuracy and cultural relevance.
Find comprehensive details in our paper.
Dataset Structure:
An example from our AMAMMERε dataset is shown:
{
"ID": "137-5-2-GH",
"Context": "Saving medium for coins.",
"Question": "Where will my kid put their pesewas in their room?",
"Option A": "A piggy bank",
"Option B": "A susu box",
"Option C": "A wishing well",
"Option D": "An asanka",
"Answer Key": "B",
"Answer Text": "A susu box",
"Country": "Ghana",
"Type": "Implicit",
"Dimension": "Social customs and Lifestyle"
}
ID: Unique identifier for the question.
Context: Scenario or premise of the question.
Question: The question text.
Options (A-D): Multiple-choice options.
Answer Key: Correct option number, if applicable.
Answer Text: Full text of the correct answer, when available.
Country: The primary country or cultural context of the question.
Type: Classification of the question as explicit or implicit in cultural context.
Dimension: The subject area or theme of the question.
OriginalDatasetID/SetNumber/QuestionNumber: Metadata tracking the question's origin.
DatasetOrigin: The specific original dataset from which the question was derived.
For any issues regarding access, please contact [acquayechristabel@gmail.com].
AMAMMERε © 2024 is licensed under CC BY 4.0
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