The task of sentiment modification requires reversing the sentiment of the
input and preserving the sentiment-independent content. However, aligned
sentences with the same content but different sentiments are usually
unavailable. Due to the lack of such parallel data, it is hard to extract
sentiment independent content and reverse the sentiment in an unsupervised way.
Previous work usually can not reconcile sentiment transformation and content
preservation. In this paper, motivated by the fact the non-emotional context
(e.g., "staff") provides strong cues for the occurrence of emotional words
(e.g., "friendly"), we propose a novel method that automatically extracts
appropriate sentiment information from learned sentiment memories according to
specific context. Experiments show that our method substantially improves the
content preservation degree and achieves the state-of-the-art performance.

本文提出了一种基于特定上下文下自动提取适当情感信息的方法来实现情感修改且保留情感无关内容的无监督学习，实验结果表明，该方法在内容保留度方面显著改进且达到了最先进水平。

学习情感记忆以实现无平行数据情感修改

Learning Sentiment Memories for Sentiment Modification without Parallel  Data

Language style transfer is the problem of migrating the content of a source
sentence to a target style. In many of its applications, parallel training data
are not available and source sentences to be transferred may have arbitrary and
unknown styles. First, each sentence is encoded into its content and style
latent representations. Then, by recombining the content with the target style,
we decode a sentence aligned in the target domain. To adequately constrain the
encoding and decoding functions, we couple them with two loss functions. The
first is a style discrepancy loss, enforcing that the style representation
accurately encodes the style information guided by the discrepancy between the
sentence style and the target style. The second is a cycle consistency loss,
which ensures that the transferred sentence should preserve the content of the
original sentence disentangled from its style. We validate the effectiveness of
our model in three tasks: sentiment modification of restaurant reviews, dialog
response revision with a romantic style, and sentence rewriting with a
Shakespearean style.

该研究论文介绍了一种利用内容和风格潜在表示进行语言风格转换的方法，并证明了该模型在餐厅评论情感修改、浪漫风格对话回复和莎士比亚风格语句重写等三种任务中具有有效性。

从具有任意未知样式的句子进行语言风格转移

Language Style Transfer from Sentences with Arbitrary Unknown Styles

This paper focuses on style transfer on the basis of non-parallel text. This
is an instance of a broad family of problems including machine translation,
decipherment, and sentiment modification. The key challenge is to separate the
content from other aspects such as style. We assume a shared latent content
distribution across different text corpora, and propose a method that leverages
refined alignment of latent representations to perform style transfer. The
transferred sentences from one style should match example sentences from the
other style as a population. We demonstrate the effectiveness of this
cross-alignment method on three tasks: sentiment modification, decipherment of
word substitution ciphers, and recovery of word order.

本文提出了一种基于非平行文本的样式转换方法，通过优化潜在表示的精细对齐来分离内容和样式，并且在情感修改、解密以及单词顺序恢复等任务上验证了该方法的有效性。