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In this paper we propose a general framework for learning distributedrepresentations of attributes: characteristics of text whose representationscan be jointly learned with word embeddings. Attributes can correspond todocument indicators (to learn sentence vectors), language indicators (to learndistributed language representations), meta-data and side information (such asthe age, gender and industry of a blogger) or representations of authors. Wedescribe a third-order model where word context and attribute vectors interactmultiplicatively to predict the next word in a sequence. This leads to thenotion of conditional word similarity: how meanings of words change whenconditioned on different attributes. We perform several experimental tasksincluding sentiment classification, cross-lingual document classification, andblog authorship attribution. We also qualitatively evaluate conditional wordneighbours and attribute-conditioned text generation.

A Multiplicative Model for Learning Distributed Text-Based Attribute Representations