论文标题
wat zei je？检测带有变化变压器的分发翻译
Wat zei je? Detecting Out-of-Distribution Translations with Variational Transformers
论文作者
论文摘要
我们使用贝叶斯深度学习等同的变压器模型来检测神经机翻译中的训练外分布句子。为此，我们开发了针对长序列离散随机变量（即输出句子中的单词）专门设计的不确定性的新量度。我们对不确定性的新度量解决了在长期句子上天真地应用现有方法的主要棘手性。我们将新措施使用新的措施，在训练有辍学的推理的变压器模型上。关于使用WMT13和Europarl的德语 - 英语翻译任务，我们表明，由于辍学不确定性，我们的措施能够识别荷兰语源句子何时使用与德语相同的单词类型的句子，而不是德语，而不是德语。
We detect out-of-training-distribution sentences in Neural Machine Translation using the Bayesian Deep Learning equivalent of Transformer models. For this we develop a new measure of uncertainty designed specifically for long sequences of discrete random variables -- i.e. words in the output sentence. Our new measure of uncertainty solves a major intractability in the naive application of existing approaches on long sentences. We use our new measure on a Transformer model trained with dropout approximate inference. On the task of German-English translation using WMT13 and Europarl, we show that with dropout uncertainty our measure is able to identify when Dutch source sentences, sentences which use the same word types as German, are given to the model instead of German.
