Optimal Transport-based Alignment of
Learned Character Representations for String Similarity
Abstract
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-ofthe-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 B3 F1
over the previous state-of-the-art approach.