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All Journal The Indonesian Journal of Computer Science
Azmi, Aqil
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Computer Science & IT Electrical & Electronics Engineering Engineering

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A Deep Learning Model for Answering Why-Questions in Arabic Azmi, Aqil; Alwaneen, Tahani
The Indonesian Journal of Computer Science Vol. 12 No. 2 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i2.3183

Abstract

The subfield of natural language processing (NLP) known as question answering (QA) involves providing answers to questions posed in natural language. Answering “why” questions has long been a challenging task for QA systems, given the complexity of the reasoning involved. In this paper, we propose a deep learning model for answering “why” questions in Arabic. Recent advances in neural network models have yielded promising results across a range of tasks, particularly with the integration of attention and memory mechanisms. Our proposed model is based on the dynamic memory network (DMN), an architecture that utilizes attention and memory mechanisms to locate and extract relevant information for answering a question. We evaluate the performance of our DMN-based model in answering Arabic “why” questions using the LEMAZA dataset, achieving an F-score of 78.61%. Our findings suggest that DMN-based models hold promise for addressing the challenge of answering “why” questions in Arabic and other languages.
LEMMA-ROUGE: An Evaluation Metric for Arabic Abstractive Text Summarization Al-Numai, Amal; Azmi, Aqil
The Indonesian Journal of Computer Science Vol. 12 No. 2 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i2.3190

Abstract

High morphological languages are characterized by complex inflections and derivations, which can present challenges for natural language processing tasks such as summarization. Abstractive text summarization aims to generate a summary by understanding the meaning of the text, rather than solely relying on the words used in the original source. However, few works address the generation of abstractive summaries due to its complexity. One of the challenges is the absence of a reliable metric to evaluate the performance of abstractive summaries. This paper proposes a lemma-based ROUGE metric and investigates the effectiveness of normalization forms in the similarity matching of the ROUGE metric for evaluating abstractive text summarization systems. We use Arabic as a case study and compare results involving different forms of the word: as is, stem-based, and lemma-based. The results show that the lemma-based form achieves higher ROUGE scores than the other forms. The findings emphasize the impact of morphological complexity on the performance of abstractive text summarization systems.
Crosslingual Transfer Learning for Arabic Story Ending Generation Alhussain, Arwa; Azmi, Aqil
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3831

Abstract

In the field of natural language processing, the task of generating story endings (SEG) requires not only a deep understanding of the narrative context but also the ability to formulate coherent conclusions. This study delves into the use of crosslingual transfer learning to address the challenges posed by the scarcity of Arabic data in SEG, proposing the utilization of extensive English story corpora as a solution. We evaluated the efficacy of multilingual models, such as mBART, mT5, and mT0, in generating Arabic story endings, assessing their performance in both zero-shot and few-shot scenarios. Despite the linguistic complexities of Arabic and the inherent challenges of crosslingual transfer, our findings demonstrate the potential of these multilingual models to transcend linguistic barriers, significantly contributing to the domain of natural language processing across different languages. This research has significant implications for generating creative text and improving multilingual natural language processing in resource-limited language contexts
Co-Authors Al-Numai, Amal Alhussain, Arwa Alwaneen, Tahani