用跨语言方法提升阿拉伯语经文问答系统性能
Cross-Language Approach for Quranic QA
- 通过机器翻译与重写扩充数据集，实现多语言对齐训练
- RoBERTa-Base在MAP@10达0.34，MRR达0.52表现最优
- 适合研究宗教文本、低资源语言问答的学者参考
问答系统在资源匮乏语言中面临严峻挑战，开发鲁棒模型尤为困难。针对《古兰经》问答系统，其重要性在于帮助超过十亿人深入理解这部圣典。然而，该系统面临独特难题：问题使用现代标准阿拉伯语，而答案源自古典阿拉伯语的经文段落，且现有数据集规模小，限制了模型表现。为此，本文提出跨语言方法：(1) 数据集增强——通过机器翻译将阿拉伯语问题转为英文，重写问题增加语言多样性，并从英文版《古兰经》中检索答案以满足多语言训练需求；(2) 语言模型微调——采用BERT-Medium、RoBERTa-Base、DeBERTa-v3-Base、ELECTRA-Large、Flan-T5、Bloom和Falcon等预训练模型。实验表明，该方法显著提升性能，RoBERTa-Base在MAP@10上达到0.34，MRR为0.52；DeBERTa-v3-Base在Recall@10（0.50）和Precision@10（0.24）上表现最佳。结果验证了跨语言策略在克服语言障碍、推动《古兰经》问答系统发展中的有效性。
原文摘要 · Abstract (English)
Question answering systems face critical limitations in languages with limited resources and scarce data, making the development of robust models especially challenging. The Quranic QA system holds significant importance as it facilitates a deeper understanding of the Quran, a Holy text for over a billion people worldwide. However, these systems face unique challenges, including the linguistic disparity between questions written in Modern Standard Arabic and answers found in Quranic verses written in Classical Arabic, and the small size of existing datasets, which further restricts model performance. To address these challenges, we adopt a cross-language approach by (1) Dataset Augmentation: expanding and enriching the dataset through machine translation to convert Arabic questions into English, paraphrasing questions to create linguistic diversity, and retrieving answers from an English translation of the Quran to align with multilingual training requirements; and (2) Language Model Fine-Tuning: utilizing pre-trained models such as BERT-Medium, RoBERTa-Base, DeBERTa-v3-Base, ELECTRA-Large, Flan-T5, Bloom, and Falcon to address the specific requirements of Quranic QA. Experimental results demonstrate that this cross-language approach significantly improves model performance, with RoBERTa-Base achieving the highest MAP@10 (0.34) and MRR (0.52), while DeBERTa-v3-Base excels in Recall@10 (0.50) and Precision@10 (0.24). These findings underscore the effectiveness of cross-language strategies in overcoming linguistic barriers and advancing Quranic QA systems
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