Impacts of Misspelled Queries on Translation and Product Search

Machine translation is used in e-commerce to translate second-language queries into the primary language of the store, to be matched by the search system against the product catalog. However, many queries contain spelling mistakes. We first present an analysis of the spelling-robustness of a population of MT systems, quantifying how spelling variations affect MT output, the list of returned products, and ultimately user behavior. We then present two sets of practical experiments illustrating how spelling-robustness may be specifically improved. For MT, reducing the number of BPE operations significantly improves spellingrobustness in six language pairs. In end-toend e-commerce, the inclusion of a dedicated spelling correction model, and the augmentation of that model's training data with languagerelevant phenomena, each improve robustness and consistency of search results.