Multilingual query localization is integral to modern e-commerce. While machine translation is widely used to translate e-commerce queries, evaluation of query translation in the context of the down-stream search task is overlooked. This study proposes a search ranking-based evaluation framework with an edit-distance based search metric to evaluate machine translation impact on cross-lingual information retrieval for e-commerce search query translation, The framework demonstrate evaluation of machine translation for e-commerce search at scale and the proposed metric is strongly associated with traditional machine translation and traditional search relevance-based metrics.

In this paper, we explored different levels of textual representations for cross-lingual information retrieval. Beyond the traditional token level representation, we adopted the subword and character level representations for information retrieval that had shown to improve neural machine translation by reducing the out-of-vocabulary issues in machine translation. We found that crosslingual information retrieval performance can be improved by combining search results from subwords and token level representation.Additionally, we improved the search performance by combining and re-ranking the result sets from the different text representations for German, French and Japanese.