  Unsupervised style transfer aims to change the style of an input sentence
while preserving its original content without using parallel training data. In
current dominant approaches, owing to the lack of fine-grained control on the
influence from the target style,they are unable to yield desirable output
sentences. In this paper, we propose a novel attentional sequence-to-sequence
(Seq2seq) model that dynamically exploits the relevance of each output word to
the target style for unsupervised style transfer. Specifically, we first
pretrain a style classifier, where the relevance of each input word to the
original style can be quantified via layer-wise relevance propagation. In a
denoising auto-encoding manner, we train an attentional Seq2seq model to
reconstruct input sentences and repredict word-level previously-quantified
style relevance simultaneously. In this way, this model is endowed with the
ability to automatically predict the style relevance of each output word. Then,
we equip the decoder of this model with a neural style component to exploit the
predicted wordlevel style relevance for better style transfer. Particularly, we
fine-tune this model using a carefully-designed objective function involving
style transfer, style relevance consistency, content preservation and fluency
modeling loss terms. Experimental results show that our proposed model achieves
state-of-the-art performance in terms of both transfer accuracy and content
preservation.
