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We introduce a task consisting in matching a proof to a given mathematicalstatement. The task fits well within current research on MathematicalInformation Retrieval and, more generally, mathematical article analysis(Mathematical Sciences, 2014). We present a dataset for the task (the MATcHdataset) consisting of over 180k statement-proof pairs extracted from modernmathematical research articles. We find this dataset highly representative ofour task, as it consists of relatively new findings useful to mathematicians.We propose a bilinear similarity model and two decoding methods to matchstatements to proofs effectively. While the first decoding method matches aproof to a statement without being aware of other statements or proofs, thesecond method treats the task as a global matching problem. Through a symbolreplacement procedure, we analyze the "insights" that pre-trained languagemodels have in such mathematical article analysis and show that while thesemodels perform well on this task with the best performing mean reciprocal rankof 73.7, they follow a relatively shallow symbolic analysis and matching toachieve that performance.

BERT is not The Count: Learning to Match Mathematical Statements with Proofs