metadata

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.