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As distributed approaches to natural language semantics have developed anddiversified, embedders for linguistic units larger than words have come to playan increasingly important role. To date, such embedders have been evaluatedusing benchmark tasks (e.g., GLUE) and linguistic probes. We propose acomparative approach, nearest neighbor overlap (N2O), that quantifiessimilarity between embedders in a task-agnostic manner. N2O requires only acollection of examples and is simple to understand: two embedders are moresimilar if, for the same set of inputs, there is greater overlap between theinputs' nearest neighbors. Though applicable to embedders of texts of any size,we focus on sentence embedders and use N2O to show the effects of differentdesign choices and architectures.

Situating Sentence Embedders with Nearest Neighbor Overlap