Amani Jamal

Hessa Abdulrahman Alawwad, Areej Alhothali, Usman Naseem et al.

Textbook question answering (TQA) is a challenging task in artificial intelligence due to the complex nature of context needed to answer complex questions. Although previous research has improved the task, there are still some limitations in textual TQA, including weak reasoning and inability to capture contextual information in the lengthy context. We propose a framework (PLRTQA) that incorporates the retrieval augmented generation (RAG) technique to handle the out-of-domain scenario where concepts are spread across different lessons, and utilize transfer learning to handle the long context and enhance reasoning abilities. Our architecture outperforms the baseline, achieving an accuracy improvement of 4. 12% in the validation set and 9. 84% in the test set for textual multiple-choice questions. While this paper focuses on solving challenges in the textual TQA, It provides a foundation for future work in multimodal TQA where the visual components are integrated to address more complex educational scenarios. Code: https://github.com/hessaAlawwad/PLR-TQA

Hessa A. Alawwad, Anas Zafar, Areej Alhothali et al.

Multimodal large language models (MLLMs) have shown success in vision-language tasks, but their ability to reason over complex educational materials remains largely untested. This work presents the first evaluation of state-of-the-art MLLMs, including LLaVA-1.5 and LLaMA 3.2-Vision, on the textbook question answering (TQA) task using the CK12-QA dataset. We introduce a multimodal retrieval-augmented generation (RAG) pipeline to simulate real-world learning by providing relevant lesson paragraphs and diagrams as context. Our zero-shot experiments reveal a critical trade-off: while retrieved context improves LLaVA's performance on text-based questions, it significantly degrades the accuracy of the more powerful LLaMA 3.2-Vision on diagram-based tasks, dropping its validation accuracy from 74.07% to 25.93%. We term this statistically significant phenomenon "catastrophic context interference." Furthermore, fine-tuning highlights architectural differences: LLaMA 3.2-Vision's performance improves to 71.16% on the test set, demonstrating its capacity to learn multimodal integration, whereas LLaVA's performance declines, indicating challenges with generalization. Our results underscore the challenges MLLMs face in modality prioritization and context integration, providing a benchmark and pointing to key directions for developing more robust AI-driven educational tools.

Salha Alyami, Amani Jamal, Areej Alhothali

Aspect-based sentiment analysis (ABSA) in natural language processing enables organizations to understand customer opinions on specific product aspects. While deep learning models are widely used for English ABSA, their application in Arabic is limited due to the scarcity of labeled data. Researchers have attempted to tackle this issue by using pre-trained contextualized language models such as BERT. However, these models are often based on fact-based data, which can introduce bias in domain-specific tasks like ABSA. To our knowledge, no studies have applied adaptive pre-training with Arabic contextualized models for ABSA. This research proposes a novel approach using domain-adaptive pre-training for aspect-sentiment classification (ASC) and opinion target expression (OTE) extraction. We examine fine-tuning strategies - feature extraction, full fine-tuning, and adapter-based methods - to enhance performance and efficiency, utilizing multiple adaptation corpora and contextualized models. Our results show that in-domain adaptive pre-training yields modest improvements. Adapter-based fine-tuning is a computationally efficient method that achieves competitive results. However, error analyses reveal issues with model predictions and dataset labeling. In ASC, common problems include incorrect sentiment labeling, misinterpretation of contrastive markers, positivity bias for early terms, and challenges with conflicting opinions and subword tokenization. For OTE, issues involve mislabeling targets, confusion over syntactic roles, difficulty with multi-word expressions, and reliance on shallow heuristics. These findings underscore the need for syntax- and semantics-aware models, such as graph convolutional networks, to more effectively capture long-distance relations and complex aspect-based opinion alignments.

Hessa Alawwad, Usman Naseem, Areej Alhothali et al.

Textbook question answering (TQA) is a complex task, requiring the interpretation of complex multimodal context. Although recent advances have improved overall performance, they often encounter difficulties in educational settings where accurate semantic alignment and task-specific document retrieval are essential. In this paper, we propose a novel approach to multimodal textbook question answering by introducing a mechanism for enhancing semantic representations through multi-objective joint training. Our model, Joint Embedding Training With Ranking Supervision for Textbook Question Answering (JETRTQA), is a multimodal learning framework built on a retriever--generator architecture that uses a retrieval-augmented generation setup, in which a multimodal large language model generates answers. JETRTQA is designed to improve the relevance of retrieved documents in complex educational contexts. Unlike traditional direct scoring approaches, JETRTQA learns to refine the semantic representations of questions and documents through a supervised signal that combines pairwise ranking and implicit supervision derived from answers. We evaluate our method on the CK12-QA dataset and demonstrate that it significantly improves the discrimination between informative and irrelevant documents, even when they are long, complex, and multimodal. JETRTQA outperforms the previous state of the art, achieving a 2.4\% gain in accuracy on the validation set and 11.1\% on the test set.

Kholoud Alsubhi, Amani Jamal, Areej Alhothali

Question answering(QA) is one of the most challenging yet widely investigated problems in Natural Language Processing (NLP). Question-answering (QA) systems try to produce answers for given questions. These answers can be generated from unstructured or structured text. Hence, QA is considered an important research area that can be used in evaluating text understanding systems. A large volume of QA studies was devoted to the English language, investigating the most advanced techniques and achieving state-of-the-art results. However, research efforts in the Arabic question-answering progress at a considerably slower pace due to the scarcity of research efforts in Arabic QA and the lack of large benchmark datasets. Recently many pre-trained language models provided high performance in many Arabic NLP problems. In this work, we evaluate the state-of-the-art pre-trained transformers models for Arabic QA using four reading comprehension datasets which are Arabic-SQuAD, ARCD, AQAD, and TyDiQA-GoldP datasets. We fine-tuned and compared the performance of the AraBERTv2-base model, AraBERTv0.2-large model, and AraELECTRA model. In the last, we provide an analysis to understand and interpret the low-performance results obtained by some models.