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We detect out-of-training-distribution sentences in Neural MachineTranslation using the Bayesian Deep Learning equivalent of Transformer models.For this we develop a new measure of uncertainty designed specifically for longsequences of discrete random variables -- i.e. words in the output sentence.Our new measure of uncertainty solves a major intractability in the naiveapplication of existing approaches on long sentences. We use our new measure ona Transformer model trained with dropout approximate inference. On the task ofGerman-English translation using WMT13 and Europarl, we show that with dropoutuncertainty our measure is able to identify when Dutch source sentences,sentences which use the same word types as German, are given to the modelinstead of German.

Wat zei je? Detecting Out-of-Distribution Translations with Variational Transformers