Technology Category
Machine Learning: Transfer, Domain Adaptation, Multi-Task LearningNatural Language Processing: Lexical Semantics and MorphologyCognitive Modeling & Cognitive Systems: Adaptive Behavior
Domain-Adaptive Pre-Training for Arabic Aspect-Based Sentiment Analysis: A Comparative Study of Domain Adaptation and Fine-Tuning Strategies
🤖 AI Summary
High-quality labeled data is scarce for Arabic aspect-based sentiment analysis (ABSA), and general-purpose pretrained language models (e.g., BERT) suffer from domain mismatch. Method: This work introduces domain-adaptive pretraining (DAPT) to Arabic contextualized language models for ABSA—the first such effort for Arabic. We systematically compare three fine-tuning strategies—feature extraction, full-parameter fine-tuning, and adapter-based fine-tuning—and conduct DAPT across multiple Arabic-domain corpora. Results: DAPT yields significant performance gains; adapter-based fine-tuning achieves the best trade-off between accuracy and computational efficiency; error analysis reveals persistent weaknesses in modeling nested, implicit, and multi-aspect semantic structures. This study establishes a reusable domain adaptation paradigm and an empirical benchmark for ABSA in low-resource languages.
Technology Category
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Limited Arabic ABSA applications due to scarce labeled data availability
Pre-trained models introduce bias from fact-based training data sources
Need syntax-aware models for complex aspect-opinion alignment challenges
Innovation
Methods, ideas, or system contributions that make the work stand out.
Domain-adaptive pre-training for Arabic sentiment analysis
Comparative study of fine-tuning and adapter methods
Proposing syntax-aware models for complex opinion relations
🔎 Similar Papers
No similar papers found.
