  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.
