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String similarity models are vital for record linkage, entity resolution, andsearch. In this work, we present STANCE --a learned model for computing thesimilarity of two strings. Our approach encodes the characters of each string,aligns the encodings using Sinkhorn Iteration (alignment is posed as aninstance of optimal transport) and scores the alignment with a convolutionalneural network. We evaluate STANCE's ability to detect whether two strings canrefer to the same entity--a task we term alias detection. We construct five newalias detection datasets (and make them publicly available). We show thatSTANCE or one of its variants outperforms both state-of-the-art and classic,parameter-free similarity models on four of the five datasets. We alsodemonstrate STANCE's ability to improve downstream tasks by applying it to aninstance of cross-document coreference and show that it leads to a 2.8 pointimprovement in B^3 F1 over the previous state-of-the-art approach.

Optimal Transport-based Alignment of Learned Character Representations for String Similarity