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We introduce AVA, an automatic evaluation approach for Question Answering,which given a set of questions associated with Gold Standard answers, canestimate system Accuracy. AVA uses Transformer-based language models to encodequestion, answer, and reference text. This allows for effectively measuring thesimilarity between the reference and an automatic answer, biased towards thequestion semantics. To design, train and test AVA, we built multiple largetraining, development, and test sets on both public and industrial benchmarks.Our innovative solutions achieve up to 74.7% in F1 score in predicting humanjudgement for single answers. Additionally, AVA can be used to evaluate theoverall system Accuracy with an RMSE, ranging from 0.02 to 0.09, depending onthe availability of multiple references.

AVA: an Automatic eValuation Approach to Question Answering Systems