Text style transfer aims to modify the style of a sentence while keeping its
content unchanged. Recent style transfer systems often fail to faithfully
preserve the content after changing the style. This paper proposes a structured
content preserving model that leverages linguistic information in the
structured fine-grained supervisions to better preserve the style-independent
content during style transfer. In particular, we achieve the goal by devising
rich model objectives based on both the sentence's lexical information and a
language model that conditions on content. The resulting model therefore is
encouraged to retain the semantic meaning of the target sentences. We perform
extensive experiments that compare our model to other existing approaches in
the tasks of sentiment and political slant transfer. Our model achieves
significant improvement in terms of both content preservation and style
transfer in automatic and human evaluation.

本文提出了一种基于结构化细粒度监督的内容保留模型，利用语言信息并设定模型目标以达到在改变句子的风格的同时更好地保留与风格无关的内容，进行情感和政治倾向转换任务的实验表明该模型在内容保留和风格转换上表现出显著改善。

无监督文本风格转移中的结构内容保持

Structured Content Preservation for Unsupervised Text Style Transfer

Following the recent success of word embeddings, it has been argued that
there is no such thing as an ideal representation for words, as different
models tend to capture divergent and often mutually incompatible aspects like
semantics/syntax and similarity/relatedness. In this paper, we show that each
embedding model captures more information than directly apparent. A linear
transformation that adjusts the similarity order of the model without any
external resource can tailor it to achieve better results in those aspects,
providing a new perspective on how embeddings encode divergent linguistic
information. In addition, we explore the relation between intrinsic and
extrinsic evaluation, as the effect of our transformations in downstream tasks
is higher for unsupervised systems than for supervised ones.

本文研究词嵌入技术中的理想表示问题，发现各种模型包含的信息互相矛盾，通过线性变换来调整相似度排序以提高其结果，同时探讨了内在和外在评估之间的关系。

揭示词嵌入中的差异性语言信息及其对内在和外在评估的意义

Uncovering divergent linguistic information in word embeddings with  lessons for intrinsic and extrinsic evaluation

How do neural language models keep track of number agreement between subject
and verb? We show that `diagnostic classifiers', trained to predict number from
the internal states of a language model, provide a detailed understanding of
how, when, and where this information is represented. Moreover, they give us
insight into when and where number information is corrupted in cases where the
language model ends up making agreement errors. To demonstrate the causal role
played by the representations we find, we then use agreement information to
influence the course of the LSTM during the processing of difficult sentences.
Results from such an intervention reveal a large increase in the language
model's accuracy. Together, these results show that diagnostic classifiers give
us an unrivalled detailed look into the representation of linguistic
information in neural models, and demonstrate that this knowledge can be used
to improve their performance.

通过使用诊断分类器来预测内部层状态，我们可以了解神经语言模型如何跟踪主语和动词之间的数的一致性，并发现诊断分类器在提高神经语言模型性能方面的独特作用。

利用诊断分类器研究和改进语言模型跟踪一致性信息的机制

Under the Hood: Using Diagnostic Classifiers to Investigate and Improve  how Language Models Track Agreement Information

We present a model of visually-grounded language learning based on stacked
gated recurrent neural networks which learns to predict visual features given
an image description in the form of a sequence of phonemes. The learning task
resembles that faced by human language learners who need to discover both
structure and meaning from noisy and ambiguous data across modalities. We show
that our model indeed learns to predict features of the visual context given
phonetically transcribed image descriptions, and show that it represents
linguistic information in a hierarchy of levels: lower layers in the stack are
comparatively more sensitive to form, whereas higher layers are more sensitive
to meaning.

基于堆叠门控循环神经网络的视觉语言学习模型，可以通过音素序列描述的图像描述，学习预测图像的视觉特征，并以层级结构表示语言信息。