Enhancing Arabic Machine Translation for E-commerce Product Information: Data Quality Challenges and Innovative Selection Approaches
Bryan Zhang, Salah Danial, Stephan Walter · 2023
Product information in e-commerce is usually localized using machine translation (MT) systems.The Arabic language has rich morphology and dialectal variations, so Arabic MT in e-commerce training requires a larger volume of data from diverse data sources; Given the dynamic nature of e-commerce, such data needs to be acquired periodically to update the MT.Consequently, validating the quality of training data periodically within an industrial setting presents a notable challenge.Meanwhile, the performance of MT systems is significantly impacted by the quality and appropriateness of the training data.Hence, this study first examines the Arabic MT in e-commerce and investigates the data quality challenges for English-Arabic MT in ecommerce then proposes heuristics-based and topic-based data selection approaches to improve MT for product information.Both online and offline experiment results have shown our proposed approaches are effective, leading to improved shopping experiences for customers.