论文标题
神经机器翻译模型的不合理波动性
The Unreasonable Volatility of Neural Machine Translation Models
论文作者
论文摘要
最近的工作表明，神经机器翻译（NMT）模型达到了令人印象深刻的性能，但是，有关理解这些模型行为的问题仍然没有得到解答。我们研究了NMT模型的意外波动，其中输入在语义和句法上是正确的。我们发现，通过对源句子的微不足道的修改，我们可以识别\ textit {意外变化}发生在翻译中的情况，而在最坏的情况下会导致误译。以令人惊讶的不同方式翻译极为相似句子的这种动荡的行为突出了当前NMT模型的潜在概括问题。我们发现RNN和变压器模型分别以26％和19％的句子变化表现出挥发性行为。
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.
