  Text attribute transfer is modifying certain linguistic attributes (e.g.
sentiment, style, authorship, etc.) of a sentence and transforming them from
one type to another. In this paper, we aim to analyze and interpret what is
changed during the transfer process. We start from the observation that in many
existing models and datasets, certain words within a sentence play important
roles in determining the sentence attribute class. These words are referred to
as \textit{the Pivot Words}. Based on these pivot words, we propose a lexical
analysis framework, \textit{the Pivot Analysis}, to quantitatively analyze the
effects of these words in text attribute classification and transfer. We apply
this framework to existing datasets and models and show that: (1) the pivot
words are strong features for the classification of sentence attributes; (2) to
change the attribute of a sentence, many datasets only requires to change
certain pivot words; (3) consequently, many transfer models only perform the
lexical-level modification, while leaving higher-level sentence structures
unchanged. Our work provides an in-depth understanding of linguistic attribute
transfer and further identifies the future requirements and challenges of this
task\footnote{Our code can be found at
https://github.com/FranxYao/pivot_analysis}.
