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Classic lexical-matching-based QA metrics are slowly being phased out becausethey punish succinct or informative outputs just because those answers were notprovided as ground truth. Recently proposed neural metrics can evaluatesemantic similarity but were trained on small textual similarity datasetsgrafted from foreign domains. We introduce the Wiki Entity Similarity (WES)dataset, an 11M example, domain targeted, semantic entity similarity datasetthat is generated from link texts in Wikipedia. WES is tailored to QAevaluation: the examples are entities and phrases and grouped into semanticclusters to simulate multiple ground-truth labels. Human annotatorsconsistently agree with WES labels, and a basic cross encoder metric is betterthan four classic metrics at predicting human judgments of correctness.

'Tis but Thy Name: Semantic Question Answering Evaluation with 11M Names for 1M Entities