论文标题
检测和理解神经机器翻译的概括障碍
Detecting and Understanding Generalization Barriers for Neural Machine Translation
论文作者
论文摘要
对看不见的实例的概括是我们对所有数据驱动模型的永恒追求。但是，对于诸如机器翻译之类的现实任务，平均衡量概括的传统方法为细粒度的概括能力提供了不良的理解。作为一种补救措施，本文试图在一个看不见的输入句子中识别并理解\ textIt {原因}细粒度概括的降解。我们提出了对概括屏障单词和一个修改版本的原则定义，该定义在计算中是可访问的。基于修改后的一种，我们提出了三种通过反事实生成的搜索感知风险估计来检测屏障检测的三种简单方法。然后，我们对两个ZH $ \ leftrightArrow $ enist基准从各个角度进行了对这些检测到的概括障碍词进行了广泛的分析。还讨论了检测到的障碍词的潜在用法。
Generalization to unseen instances is our eternal pursuit for all data-driven models. However, for realistic task like machine translation, the traditional approach measuring generalization in an average sense provides poor understanding for the fine-grained generalization ability. As a remedy, this paper attempts to identify and understand generalization barrier words within an unseen input sentence that \textit{cause} the degradation of fine-grained generalization. We propose a principled definition of generalization barrier words and a modified version which is tractable in computation. Based on the modified one, we propose three simple methods for barrier detection by the search-aware risk estimation through counterfactual generation. We then conduct extensive analyses on those detected generalization barrier words on both Zh$\Leftrightarrow$En NIST benchmarks from various perspectives. Potential usage of the detected barrier words is also discussed.
