Distributional representations of words, also known as word vectors, have
become crucial for modern natural language processing tasks due to their wide
applications. Recently, a growing body of word vector postprocessing algorithm
has emerged, aiming to render off-the-shelf word vectors even stronger. In line
with these investigations, we introduce a novel word vector postprocessing
scheme under a causal inference framework. Concretely, the postprocessing
pipeline is realized by Half-Sibling Regression (HSR), which allows us to
identify and remove confounding noise contained in word vectors. Compared to
previous work, our proposed method has the advantages of interpretability and
transparency due to its causal inference grounding. Evaluated on a battery of
standard lexical-level evaluation tasks and downstream sentiment analysis
tasks, our method reaches state-of-the-art performance.

本文提出了一种基于因果推断框架下的新型词向量后处理方案，并应用 Half-Sibling Regression 算法，以识别和消除噪音，实现对预训练词向量的增强，相较于之前的方法具有可解释性和透明度，评估结果表明在标准水平评估任务和情感分析方面的性能达到了最先进水平。