In this paper we propose a general framework for learning distributed
representations of attributes: characteristics of text whose representations
can be jointly learned with word embeddings. Attributes can correspond to
document indicators (to learn sentence vectors), language indicators (to learn
distributed language representations), meta-data and side information (such as
the age, gender and industry of a blogger) or representations of authors. We
describe a third-order model where word context and attribute vectors interact
multiplicatively to predict the next word in a sequence. This leads to the
notion of conditional word similarity: how meanings of words change when
conditioned on different attributes. We perform several experimental tasks
including sentiment classification, cross-lingual document classification, and
blog authorship attribution. We also qualitatively evaluate conditional word
neighbours and attribute-conditioned text generation.

通过在单词嵌入中联合学习文本特征的分布式表征的一般框架，我们提出了一种第三阶段模型，其中单词环境和属性向量相互作用以预测序列中的下一个单词，并通过情感分类、跨语言文本分类和博客作者归因等多个实验任务进行了评估。