AKER: Arabic Knowledge-enriched Reader for Machine Reading Comprehension
Eman Albilali, Nora Al-Twairesh, Manar Hosny · ACM Transactions on Asian and Low-Resource Language Information Processing · 2025
Machine reading comprehension aims at understanding a passage and answer a given question by selecting a span from the passage. Recently, pre-trained language models achieved state-of-the-art results on Arabic machine reading comprehension, yet a broad body of works suggests that BERT-based variant models fail to encode and associate common sense facts and world knowledge. To alleviate this weakness, we propose an Arabic knowledge enriched reader model, which fuses external knowledge into contextual representation using an attention and gating mechanism. We learn and generate Arabic knowledge graph embeddings that represent information from Arabic Wikidata and utilize this representation when fusing knowledge. We adopted a knowledge graph embedding scoring function to select the most relevant concepts to the context from the knowledge graph. We evaluated our approach on multiple Arabic machine reading comprehension datasets. Despite leveraging a comparatively smaller pre-trained language model, our approach significantly outperforms large language models in Arabic machine reading comprehension across multiple benchmark datasets, achieving substantial gains in both EM and F1 scores.