Word vectors are at the core of many natural language processing tasks.
Recently, there has been interest in post-processing word vectors to enrich
their semantic information. In this paper, we introduce a novel word vector
post-processing technique based on matrix conceptors (Jaeger2014), a family of
regularized identity maps. More concretely, we propose to use conceptors to
suppress those latent features of word vectors having high variances. The
proposed method is purely unsupervised: it does not rely on any corpus or
external linguistic database. We evaluate the post-processed word vectors on a
battery of intrinsic lexical evaluation tasks, showing that the proposed method
consistently outperforms existing state-of-the-art alternatives. We also show
that post-processed word vectors can be used for the downstream natural
language processing task of dialogue state tracking, yielding improved results
in different dialogue domains.

本文介绍了一种基于矩阵嵌入器的新型词向量后处理技术，能够抑制词向量中高方差的潜在特征，该方法是纯无监督学习的，不依赖于任何语料库或外部语言数据库，研究结果表明，这种词向量后处理方法在内在词汇评估任务中表现比现有的前沿方法更好，并且后处理后的词向量可用于对话状态跟踪的下游自然语言处理任务，在不同的对话领域中都能取得更好的结果。