神经机器翻译模型的不合理波动性
The Unreasonable Volatility of Neural Machine Translation Models
摘要：
最近的工作表明，神经机器翻译（NMT）模型实现了令人印象深刻的性能，然而，关于理解这些模型的行为的问题仍然没有答案。我们研究了NMT模型的意外波动性，其中输入在语义和语法上是正确的。我们发现，通过对源句的微小修改，我们可以识别出\textit{unexpected changes}在翻译中发生的情况，最坏的情况下会导致误译。这种以惊人的不同方式翻译极其相似的句子的不稳定行为突出了当前NMT模型的潜在泛化问题。我们发现，RNN和Transformer模型分别在26%和19%的句子变化中显示出不稳定的行为。
英文摘要：
Recent works have shown that Neural Machine Translation (NMT) models achieve impressive performance, however, questions about understanding the behavior of these models remain unanswered. We investigate the unexpected volatility of NMT models where the input is semantically and syntactically correct. We discover that with trivial modifications of source sentences, we can identify cases where \textit{unexpected changes} happen in the translation and in the worst case lead to mistranslations. This volatile behavior of translating extremely similar sentences in surprisingly different ways highlights the underlying generalization problem of current NMT models. We find that both RNN and Transformer models display volatile behavior in 26% and 19% of sentence variations, respectively.
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