machine translation has made rapid advances in recent years. Millions of
people are using it today in online translation systems and mobile applications
in order to communicate across language barriers. The question naturally arises
whether such systems can approach or achieve parity w
研究发现，与人类翻译相比，神经机器翻译包含了更多与频率较高的词强相关的套话序列，但与频率较低的词弱相关的套话序列较少。本文尝试使用议会语料库复制这项研究，结果显示不同的神经机器系统之间存在差异，其中 Google 翻译所包含的强相关二元组较少，而 Deepl 和 Microsoft 的翻译结果则较为相似。