README.md

---
license: apache-2.0
task_categories:
- question-answering
language:
- am
pretty_name: AmaSquad
---
# AmaSQuAD - Amharic Question Answering Dataset

## Dataset Overview
AmaSQuAD is a synthetic dataset created by translating the SQuAD 2.0 dataset into Amharic using a novel translation framework. The dataset addresses key challenges, including:  
- Misalignment between translated questions and answers.  
- Presence of multiple answers in the translated context.  

Techniques such as cosine similarity (using embeddings from a fine-tuned Amharic BERT model) and Longest Common Subsequence (LCS) were used to ensure high-quality alignment between questions and answers.  

## Key Features
- **Language**: Amharic, a widely spoken Semitic language with limited NLP resources.  
- **Data Size**: Includes training and development sets based on SQuAD 2.0, tailored for extractive machine reading comprehension.  
- **Use Case**: Designed for training and evaluating Amharic Question Answering systems, particularly extractive QA models.  

## Applications
- Developing and benchmarking machine reading comprehension models for Amharic.  
- Bridging the resource gap in low-resource language NLP research.  

## Caveats
- As a synthetic dataset, some translation-induced artifacts may be present.  
- The dataset complements but does not replace the need for human-curated Amharic QA datasets.  

## Citation
If you use this dataset, please cite:  
Hailemariam, N. D., Guda, B., & Tefferi, T. *XLM-R Based Extractive Amharic Question Answering with AmaSQuAD*. Carnegie Mellon University.