Question-Answering (QA) Model for a Personalized Learning Assistant for Arabic Language

• URL: http://arxiv.org/abs/2406.08519v1
• Date: Tue, 11 Jun 2024 20:23:31 GMT
• Title: Question-Answering (QA) Model for a Personalized Learning Assistant for Arabic Language
• Abstract summary: This paper describes the creation, optimization, and assessment of a question-answering (QA) model for a personalized learning assistant that uses BERT transformers customized for the Arabic language. Our approach uses BERT's brilliant capabilities to automatically produce correct answers to questions in the field of science education. The model's ability to understand and extract pertinent information is improved by finetuning it using 11th and 12th grade biology book in Palestinian curriculum.
• Score: 4.79071544824946
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper describes the creation, optimization, and assessment of a question-answering (QA) model for a personalized learning assistant that uses BERT transformers customized for the Arabic language. The model was particularly finetuned on science textbooks in Palestinian curriculum. Our approach uses BERT's brilliant capabilities to automatically produce correct answers to questions in the field of science education. The model's ability to understand and extract pertinent information is improved by finetuning it using 11th and 12th grade biology book in Palestinian curriculum. This increases the model's efficacy in producing enlightening responses. Exact match (EM) and F1 score metrics are used to assess the model's performance; the results show an EM score of 20% and an F1 score of 51%. These findings show that the model can comprehend and react to questions in the context of Palestinian science book. The results demonstrate the potential of BERT-based QA models to support learning and understanding Arabic students questions.

Related papers

• Fine-Tuning BERT for Domain-Specific Question Answering: Toward Educational NLP Resources at University Scale [0.0]
  The University of Limerick's Department of Electronic and Computer Engineering developed a robot to provide course information to students.<n>A custom dataset of 1,203 question-answer pairs in SQuAD format was constructed using the university book of modules, supplemented with manually and synthetically generated entries.<n>Results show that even modest fine-tuning improves hypothesis framing and knowledge extraction.<n>Our work addresses this gap by showing that fine-tuning BERT with academic pairs yields effective results.
  arXiv  Detail & Related papers  (2025-12-04T18:27:46Z)
• How well can LLMs Grade Essays in Arabic? [3.101490720236325]
  This research assesses the effectiveness of large language models (LLMs) in the task of Arabic automated essay scoring (AES) using the AR-AES dataset. It explores various evaluation methodologies, including zero-shot, few-shot in-context learning, and fine-tuning. A mixed-language prompting strategy, integrating English prompts with Arabic content, was implemented to improve model comprehension and performance.
  arXiv  Detail & Related papers  (2025-01-27T21:30:02Z)
• Unlocking the Potential of Multiple BERT Models for Bangla Question Answering in NCTB Textbooks [0.0]
  evaluating text comprehension in educational settings is critical for understanding student performance and improving curricular effectiveness. This study investigates the capability of state-of-the-art language models-RoBERTa Base, Bangla-BERT, and BERT Base-in automatically assessing Bangla passage-based question-answering from the National Curriculum and Textbook Board (NCTB) textbooks for classes 6-10.
  arXiv  Detail & Related papers  (2024-12-24T13:59:23Z)
• GemmAr: Enhancing LLMs Through Arabic Instruction-Tuning [0.0]
  We introduce InstAr-500k, a new Arabic instruction dataset created by generating and collecting content. We assess this dataset by fine-tuning an open-source Gemma-7B model on several downstream tasks to improve its functionality. Based on multiple evaluations, our fine-tuned model achieves excellent performance on several Arabic NLP benchmarks.
  arXiv  Detail & Related papers  (2024-07-02T10:43:49Z)
• The Death of Feature Engineering? BERT with Linguistic Features on SQuAD 2.0 [16.95168095046929]
  We develop an end-to-end question answering model incorporating BERT and additional linguistic features. The EM score and F1 score are improved 2.17 and 2.14 compared with BERT(base)
  arXiv  Detail & Related papers  (2024-04-04T03:50:34Z)
• Enhancing Essay Scoring with Adversarial Weights Perturbation and Metric-specific AttentionPooling [18.182517741584707]
  This study explores the application of BERT-related techniques to enhance the assessment of ELLs' writing proficiency. To address the specific needs of ELLs, we propose the use of DeBERTa, a state-of-the-art neural language model.
  arXiv  Detail & Related papers  (2024-01-06T06:05:12Z)
• Physics of Language Models: Part 3.2, Knowledge Manipulation [51.68385617116854]
  This paper investigates four fundamental knowledge manipulation tasks. We show that language models excel in knowledge retrieval but struggle even in the simplest classification or comparison tasks. Our findings also apply to modern pretrained language models such as GPT-4.
  arXiv  Detail & Related papers  (2023-09-25T17:50:41Z)
• AceGPT, Localizing Large Language Models in Arabic [73.39989503874634]
  The paper proposes a comprehensive solution that includes pre-training with Arabic texts, Supervised Fine-Tuning (SFT) utilizing native Arabic instructions, and GPT-4 responses in Arabic. The goal is to cultivate culturally cognizant and value-aligned Arabic LLMs capable of accommodating the diverse, application-specific needs of Arabic-speaking communities.
  arXiv  Detail & Related papers  (2023-09-21T13:20:13Z)
• Cross-Lingual NER for Financial Transaction Data in Low-Resource Languages [70.25418443146435]
  We propose an efficient modeling framework for cross-lingual named entity recognition in semi-structured text data. We employ two independent datasets of SMSs in English and Arabic, each carrying semi-structured banking transaction information. With access to only 30 labeled samples, our model can generalize the recognition of merchants, amounts, and other fields from English to Arabic.
  arXiv  Detail & Related papers  (2023-07-16T00:45:42Z)
• Context Matters: A Strategy to Pre-train Language Model for Science Education [4.053049694533914]
  BERT-based language models have shown significant superiority over traditional NLP models in various language-related tasks. The language used by students is different from the language in journals and Wikipedia, which are training sources of BERT. Our study confirms the effectiveness of continual pre-training on domain-specific data in the education domain.
  arXiv  Detail & Related papers  (2023-01-27T23:50:16Z)
• Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering [124.16250115608604]
  We present Science Question Answering (SQA), a new benchmark that consists of 21k multimodal multiple choice questions with a diverse set of science topics and annotations of their answers with corresponding lectures and explanations. We show that SQA improves the question answering performance by 1.20% in few-shot GPT-3 and 3.99% in fine-tuned UnifiedQA. Our analysis further shows that language models, similar to humans, benefit from explanations to learn from fewer data and achieve the same performance with just 40% of the data.
  arXiv  Detail & Related papers  (2022-09-20T07:04:24Z)
• Fantastic Questions and Where to Find Them: FairytaleQA -- An Authentic Dataset for Narrative Comprehension [136.82507046638784]
  We introduce FairytaleQA, a dataset focusing on narrative comprehension of kindergarten to eighth-grade students. FairytaleQA consists of 10,580 explicit and implicit questions derived from 278 children-friendly stories.
  arXiv  Detail & Related papers  (2022-03-26T00:20:05Z)
• Pre-trained Transformer-Based Approach for Arabic Question Answering : A Comparative Study [0.5801044612920815]
  We evaluate the state-of-the-art pre-trained transformers models for Arabic QA using four reading comprehension datasets. We fine-tuned and compared the performance of the AraBERTv2-base model, AraBERTv0.2-large model, and AraELECTRA model.
  arXiv  Detail & Related papers  (2021-11-10T12:33:18Z)
• Evaluation Toolkit For Robustness Testing Of Automatic Essay Scoring Systems [64.4896118325552]
  We evaluate the current state-of-the-art AES models using a model adversarial evaluation scheme and associated metrics. We find that AES models are highly overstable. Even heavy modifications(as much as 25%) with content unrelated to the topic of the questions do not decrease the score produced by the models.
  arXiv  Detail & Related papers  (2020-07-14T03:49:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.