  Although deep convolutional networks have achieved improved performance in
many natural language tasks, they have been treated as black boxes because they
are difficult to interpret. Especially, little is known about how they
represent language in their intermediate layers. In an attempt to understand
the representations of deep convolutional networks trained on language tasks,
we show that individual units are selectively responsive to specific morphemes,
words, and phrases, rather than responding to arbitrary and uninterpretable
patterns. In order to quantitatively analyze such an intriguing phenomenon, we
propose a concept alignment method based on how units respond to the replicated
text. We conduct analyses with different architectures on multiple datasets for
classification and translation tasks and provide new insights into how deep
models understand natural language.
