Summary of A Benchmark Dataset with Larger Context For Non-factoid Question Answering Over Islamic Text, by Faiza Qamar et al.
A Benchmark Dataset with Larger Context for Non-Factoid Question Answering over Islamic Text
by Faiza Qamar, Seemab Latif, Rabia Latif
First submitted to arxiv on: 15 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers address the need for efficient and accurate question-answering (QA) systems for accessing and comprehending religious texts, particularly the Quran and Ahadith. They introduce a comprehensive dataset comprising over 73,000 question-answer pairs, meticulously enriched with contextual information, serving as a valuable resource for training and evaluating tailored QA systems. The researchers highlight the contributions of their dataset and establish a benchmark for evaluating QA performance in the Quran and Ahadith domains. This paper presents significant findings regarding the limitations of existing automatic evaluation techniques. A human evaluation reveals critical insights into the discrepancies between automatic evaluation metrics, such as ROUGE scores, and human assessments. The study underscores the necessity for evaluation techniques that capture the nuances and complexities inherent in understanding religious texts, surpassing the limitations of traditional automatic metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary For curious learners or general audiences without a technical background, this paper is about creating better systems to answer questions about religious texts like the Quran and Ahadith. The researchers want to help people find answers quickly and accurately, but they’re facing challenges because there aren’t many QA systems designed specifically for these types of texts. To address this gap, they’ve created a massive dataset with over 73,000 question-answer pairs that provides important context information. The study highlights the importance of having better evaluation methods to assess how well AI systems perform in understanding religious texts. They found that automatic evaluation metrics can be misleading and don’t always match up with human experts’ judgments. This shows us that we need more advanced methods to evaluate AI systems’ performance when it comes to complex tasks like understanding religious texts. |
Keywords
» Artificial intelligence » Question answering » Rouge
