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