Understanding the inner workings of neural network models is a crucial step
for rationalizing their output and refining their architecture.
Transformer-based models are the core of recent natural language processing and
have been analyzed typically with attention patterns as their epoch-making
feature is contextualizing surrounding input words via attention mechanisms. In
this study, we analyze their inner contextualization by considering all the
components, including the feed-forward block (i.e., a feed-forward layer and
its surrounding residual and normalization layers) as well as the attention.
Our experiments with masked language models show that each of the previously
overlooked components did modify the degree of the contextualization in case of
processing special word-word pairs (e.g., consisting of named entities).
Furthermore, we find that some components cancel each other's effects. Our
results could update the typical view about each component's roles (e.g.,
attention performs contextualization, and the other components serve different
roles) in the Transformer layer.

通过对 Transformer 模型内部结构的分析，探讨了每个组件对文本语境化的作用，实验发现每个组件都对不同程度的语境化产生了影响，并且有些组件的作用相互抵消，更新了我们关于 Transformer 层各个组件作用的常见认识。