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Natural language processing (NLP) systems are increasingly trained togenerate open-ended text rather than classifying between responses. This makesresearch on evaluation metrics for generated language -- functions that scoresystem output given the context and/or human reference responses -- of criticalimportance. However, different metrics have different strengths and biases, andreflect human intuitions better on some tasks than others. There is currentlyno simple, unified way to compare, analyse or evaluate metrics across arepresentative set of tasks. Here, we describe the Benchmark to EvaluateAutomatic Metrics (BEAMetrics), a resource to make research into new metricsitself easier to evaluate. BEAMetrics users can quickly compare existing andnew metrics with human judgements across a diverse set of tasks, qualitydimensions (fluency vs. coherence vs. informativeness etc), and languages. Asgeneration experts might predict, BEAMetrics reveals stark task-dependentdifferences between existing metrics, and consistently poor performance ontasks with complex answer spaces or high reliance on general knowledge. Whilethis analysis highlights a critical issue facing current research practice,BEAMetrics also contribute to its resolution by facilitating research intobetter metrics -- particularly those that can account for the complexinteraction between context and general knowledge inherent to many modern NLPapplications. BEAMetrics is available under the MIT License:https://github.com/ThomasScialom/BEAMetrics

BEAMetrics: A Benchmark for Language Generation Evaluation Evaluation