The Inception Team presented a system for Semantic Question Similarity in Arabic as part of the NSURL 2019 Task 8. The system explores different methods for determining question similarity in Arabic. Their best result was an ensemble model using a pre-trained multilingual BERT model, achieving a 95.924% F1-Score and ranking first among nine participating teams. Why it matters: This demonstrates strong performance on a key Arabic NLP task, advancing the state-of-the-art in semantic understanding for the language.
This paper introduces a nested embedding learning framework for Arabic NLP, utilizing Matryoshka Embedding Learning and multilingual models. The authors translated sentence similarity datasets into Arabic to enable comprehensive evaluation. Experiments on the Arabic Natural Language Inference dataset show Matryoshka embedding models outperform traditional models by 20-25% in capturing Arabic semantic nuances. Why it matters: This work advances Arabic NLP by providing a new method and evaluation benchmark for semantic similarity, which is crucial for tasks like information retrieval and text understanding.
Researchers developed a semantic search tool for the Quran using Arabic NLP techniques. The tool was trained on a dataset of over 30 tafsirs (interpretations) of the Quran. Using the SNxLM model and cosine similarity, the tool identifies Quranic verses most relevant to a user's query, achieving a similarity score of up to 0.97. Why it matters: This tool could significantly improve access to the Quran's teachings for Arabic speakers and researchers, providing a valuable resource for religious study and understanding.
This paper presents a comparative study of pre-trained transformer models for Arabic question answering (QA). The study evaluates the performance of AraBERTv2-base, AraBERTv0.2-large, and AraELECTRA models on four reading comprehension datasets: Arabic-SQuAD, ARCD, AQAD, and TyDiQA-GoldP. The researchers fine-tuned these models and analyzed the results to understand the performance disparities. Why it matters: This research contributes to the advancement of Arabic NLP by evaluating and comparing state-of-the-art models on important QA tasks, addressing the scarcity of resources in this domain.
This paper introduces an enhanced Dense Passage Retrieval (DPR) framework tailored for Arabic text retrieval. The core innovation is an Attentive Relevance Scoring (ARS) mechanism that improves semantic relevance modeling between questions and passages, replacing standard interaction methods. The method integrates pre-trained Arabic language models and architectural refinements, achieving improved retrieval and ranking accuracy for Arabic question answering. Why it matters: This work addresses the underrepresentation of Arabic in NLP research by providing a novel approach and publicly available code to improve Arabic text retrieval, which can benefit various applications like Arabic search engines and question-answering systems.
This paper introduces MOTOR, a multimodal retrieval and re-ranking approach for medical visual question answering (MedVQA) that uses grounded captions and optimal transport to capture relationships between queries and retrieved context, leveraging both textual and visual information. MOTOR identifies clinically relevant contexts to augment VLM input, achieving higher accuracy on MedVQA datasets. Empirical analysis shows MOTOR outperforms state-of-the-art methods by an average of 6.45%.
Researchers introduce a new task for generating question-passage pairs to aid in developing regulatory question-answering (QA) systems. The ObliQA dataset, comprising 27,869 questions from Abu Dhabi Global Markets (ADGM) financial regulations, is presented. A baseline Regulatory Information Retrieval and Answer Generation (RIRAG) system is designed and evaluated using the RePASs metric.