Abstract: A hierarchical embedding model is used to obtain respective language-agnostic embeddings of entity records of a cross-language data set. A plurality of record representation pairs is prepared based at least in part on the language-agnostic embeddings. A machine learning model is trained using the record representations pairs to generate similarity scores for pairs of entity records whose text attributes are expressed in different languages.

Abstract: A hierarchical embedding model is used to obtain respective language-agnostic embeddings of entity records of a cross-language data set. A plurality of record representation pairs is prepared based at least in part on the language-agnostic embeddings. A machine learning model is trained using the record representations pairs to generate similarity scores for pairs of entity records whose text attributes are expressed in different languages.

Abstract: Systems and methods are provided for using language-agnostic embedding data to analyze a plurality of items associated with an item identifier in a catalog and determine a characteristic associated with the plurality of items or the item identifier. A distribution may be generated based on the language-agnostic embedding data and groups of items may be identified based on the distribution. Based on the groups of items, the item identifier may be classified as a consistent item identifier or an inconsistent item identifier across the catalog. A primary group of items and secondary groups of items can be identified for the plurality of items. The primary group of items may include items with a verified association with the item identifier and the secondary groups of items may include items with an unverified association with the item identifier.