Enhancing A rabic Machine Translation for E -commerce Product Information: Data Quality Challenges and Innovative Selection Approaches (2023)

Abstract: Product information in e-commerce is usually localized using machine translation (MT) systems. 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 e-commerce 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.

## introduction as e-commerce shopping websites are localized worldwide, customers now are provided with options to browse products in their preferred languages other than the primary language of the store. for instance, customers from the kingdom of saudi arabia (ksa) can shop in both english and arabic in the ksa store. modern e-commerce stores provide multi-lingual product discovery @xcite @xcite @xcite , and product information such as titles, descriptions, and bulletpoints are usually translated using machine translation (mt) systems @xcite @xcite . product information in e-commerce demands ac-curate, culturally relevant, and contextually appropriate translations, which has significant impact on the customers' shopping experiences. the highly complex morphology of arabic as well as other linguistic aspects have made the machine translation from and to arabic a lot more challenging @xcite . moreover, the multitude of dialectal variants along social and geographic dimensions introduce further linguistic challenges to mt @xcite . hence in order to train arabic mt systems in the e-commerce industrial setting, typically a larger volume of training data needs to be acquired from a wider range of data sources to address the complexity of the arabic language. moreover, as ecommerce product catalogs continue to expand, the task of maintaining up-to-date machine translation systems poses significant challenges. when the vast amount of product information is sourced from various sellers or suppliers, each can present the data differently. as a result, the inconsistencies and noise in the source data can have a negative impact on mt systems. meanwhile, validating a substantial volume of data for mt training at scale becomes increasingly difficult and timeconsuming, demanding significant resources for manual review and error correction to guarantee the accurate interpretation of product information. therefore, in this study, we first investigate the training data quality issues and challenges for arabic mt in e-commerce, and identify two major data issue patterns based on our observations and addressing the data quality challenges from the periodic data acquisition. then we propose heuristics-based and topic-based data selection approaches for arabic mt. the heuristics-based data selection approach leverages the identified data issue patterns that are typical to the arabic training data in e-commerce and proposes straightforward and effective data filters to remove the undesirable noisy data for training data quality improvement; the topic-based data selection approach first clusters the data based on the textual patterns then choose the clusters of the clean data for mt training so that the data of new and unknown noise patterns from the periodic data sourcing can be removed. we experiment our proposed approaches separately and in combination for the case study of english-arabic mt. the offline experiment results have shown that the application of two approaches in combination can further improve the mt by 4.47% for bleu on average across three domains (product titles, descriptions and bulletpoints), and 9.32% for bleu for titles. the online a/b experiment results further have shown the customers' shopping experiences have been improved, which indicates the effectiveness of our proposed approaches. ## related work there are studies related to data selection for machine translation systems. @xcite focuses on data selection for curriculum training through fine-tuning mt model on a selected by both deterministic scoring, @xcite proposes dynamic data selection which varies the selected subset of training data between different training epochs to improve neural mt. previous studies also have successfully used topic models to improve statistical machine translation @xcite @xcite and neural machine translation @xcite . @xcite integrates topic models as feature functions in the phrase-tables to improve statistical machine translation for ecommerce domain adaption. @xcite presents an approach using topic models to increase the likelihood of word selection from the same topic as the source context. instead of explicitly affecting the parameters or vocabulary selection, in this paper, we utilize a topical cluster model for data selection. ## conclusion in this study, we first review and investigate the data quality validation challenges the arabic machine translation systems for product information translation in e-commerce, arabic language has rich morphology and dialectal variations, which can cause more data quality issues that are unique to acquired training data for developing mt translating from and to arabic. then we propose heuristics-based and topic-based data selection approaches to select clean and desirable data for neural mt training. both offline experiment results and human evaluation have shown both approaches can improve the english-arabic mt for product information. on-line a/b testing also shows customers' shopping experience has been improved with the translation from the mt trained with two approaches, which it shows the effectiveness of our proposed approaches.