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There is a niche of companies responsible for intermediating the purchase oflarge batches of varied products for other companies, for which the mainchallenge is to perform product description standardization, i.e., matching anitem described by a client with a product described in a catalog. The problemis complex since the client's product description may be: (1) potentiallynoisy; (2) short and uninformative (e.g., missing information about model andsize); and (3) cross-language. In this paper, we formalize this problem as aranking task: given an initial client product specification (query), return themost appropriate standardized descriptions (response). In this paper, wepropose TPDR, a two-step Transformer-based Product and Class DescriptionRetrieval method that is able to explore the semantic correspondence between ISand SD, by exploiting attention mechanisms and contrastive learning. First,TPDR employs the transformers as two encoders sharing the embedding vectorspace: one for encoding the IS and another for the SD, in which correspondingpairs (IS, SD) must be close in the vector space. Closeness is further enforcedby a contrastive learning mechanism leveraging a specialized loss function.TPDR also exploits a (second) re-ranking step based on syntactic features thatare very important for the exact matching (model, dimension) of certainproducts that may have been neglected by the transformers. To evaluate ourproposal, we consider 11 datasets from a real company, covering differentapplication contexts. Our solution was able to retrieve the correctstandardized product before the 5th ranking position in 71% of the cases andits correct category in the first position in 80% of the situations. Moreover,the effectiveness gains over purely syntactic or semantic baselines reach up to3.7 times, solving cases that none of the approaches in isolation can do bythemselves.

TPDR: A Novel Two-Step Transformer-based Product and Class Description Match and Retrieval Method