In traditional Distributional Semantic Models (DSMs) the multiple senses of a
polysemous word are conflated into a single vector space representation. In
this work, we propose a DSM that learns multiple distributional representations
of a word based on different topics. First, a separate DSM is trained for each
topic and then each of the topic-based DSMs is aligned to a common vector
space. Our unsupervised mapping approach is motivated by the hypothesis that
words preserving their relative distances in different topic semantic
sub-spaces constitute robust \textit{semantic anchors} that define the mappings
between them. Aligned cross-topic representations achieve state-of-the-art
results for the task of contextual word similarity. Furthermore, evaluation on
NLP downstream tasks shows that multiple topic-based embeddings outperform
single-prototype models.

本文提出了一种基于不同主题学习单词的多个分布式表示的分布式语义模型，该模型通过不同主题训练不同的 DSM，然后将每个基于主题的 DSM 对齐到一个公共向量空间，实现了最先进的上下文词语相似性任务，并且在 NLP 下游任务中表现出比单一原型模型更好的性能。