  Identifying semantically equivalent sentences is important for many
cross-lingual and mono-lingual NLP tasks. Current approaches to semantic
equivalence take a loose, sentence-level approach to "equivalence," despite
previous evidence that fine-grained differences and implicit content have an
effect on human understanding (Roth and Anthonio, 2021) and system performance
(Briakou and Carpuat, 2021). In this work, we introduce a novel, more sensitive
method of characterizing semantic equivalence that leverages Abstract Meaning
Representation graph structures. We develop an approach, which can be used with
either gold or automatic AMR annotations, and demonstrate that our solution is
in fact finer-grained than existing corpus filtering methods and more accurate
at predicting strictly equivalent sentences than existing semantic similarity
metrics. We suggest that our finer-grained measure of semantic equivalence
could limit the workload in the task of human post-edited machine translation
and in human evaluation of sentence similarity.
