  Style is ubiquitous in our daily language uses, while what is language style
to learning machines? In this paper, by exploiting the second-order statistics
of semantic vectors of different corpora, we present a novel perspective on
this question via style matrix, i.e. the covariance matrix of semantic vectors,
and explain for the first time how Sequence-to-Sequence models encode style
information innately in its semantic vectors. As an application, we devise a
learning-free text style transfer algorithm, which explicitly constructs a pair
of transfer operators from the style matrices for style transfer. Moreover, our
algorithm is also observed to be flexible enough to transfer out-of-domain
sentences. Extensive experimental evidence justifies the informativeness of
style matrix and the competitive performance of our proposed style transfer
algorithm with the state-of-the-art methods.
