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.

本文提出一种基于注意力机制的序列到序列模型，采用无监督方式实现风格迁移，通过预测每个输出词语与目标风格的相关性实现精细的控制，进而使用神经风格组件进行风格迁移，该模型在风格迁移的准确性和内容保留方面达到了最先进的水平。

探索上下文词级风格相关性用于无监督风格转移

Exploring Contextual Word-level Style Relevance for Unsupervised Style  Transfer

Draft of textbook chapter on neural machine translation. a comprehensive
treatment of the topic, ranging from introduction to neural networks,
computation graphs, description of the currently dominant attentional
sequence-to-sequence model, recent refinements, alternative architectures and
challenges. Written as chapter for the textbook Statistical Machine
Translation. Used in the JHU Fall 2017 class on machine translation.

本文介绍神经机器翻译的相关概念，包括神经网络、注意力模型、模型改进和替代结构的挑战。作为《统计机器翻译》的教材章节，用于 JHU 2017 年秋季机器翻译课程。