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。

利用半同胞回归对词嵌入进行因果去噪

Causally Denoise Word Embeddings Using Half-Sibling Regression

We describe a method for removing the effect of confounders in order to
reconstruct a latent quantity of interest. The method, referred to as
half-sibling regression, is inspired by recent work in causal inference using
additive noise models. We provide a theoretical justification and illustrate
the potential of the method in a challenging astronomy application.

本文介绍了一种通过使用半同胞回归方法来消除混杂因素以重构潜在兴趣量的方法，在因果推断领域中引人注目，是一种有理理论基础的方法，并体现了在具有挑战性的天文学应用中的潜力。