Embedding spaces contain interpretable dimensions indicating gender,
formality in style, or even object properties. This has been observed multiple
times. Such interpretable dimensions are becoming valuable tools in different
areas of study, from social science to neuroscience. The standard way to
compute these dimensions uses contrasting seed words and computes difference
vectors over them. This is simple but does not always work well. We combine
seed-based vectors with guidance from human ratings of where words fall along a
specific dimension, and evaluate on predicting both object properties like size
and danger, and the stylistic properties of formality and complexity. We obtain
interpretable dimensions with markedly better performance especially in cases
where seed-based dimensions do not work well.

使用种子词义空间和人类评价指导相结合的方法，在预测物体属性和风格属性方面，表现出了明显更好的性能，尤其是在种子词义空间不能很好工作的情况下。

用人类判断调整嵌入空间中可解释维度

Adjusting Interpretable Dimensions in Embedding Space with Human  Judgments

Word embeddings are useful for a wide variety of tasks, but they lack
interpretability. By rotating word spaces, interpretable dimensions can be
identified while preserving the information contained in the embeddings without
any loss. In this work, we investigate three methods for making word spaces
interpretable by rotation: Densifier (Rothe et al., 2016), linear SVMs and
DensRay, a new method we propose. In contrast to Densifier, DensRay can be
computed in closed form, is hyperparameter-free and thus more robust than
Densifier. We evaluate the three methods on lexicon induction and set-based
word analogy. In addition we provide qualitative insights as to how
interpretable word spaces can be used for removing gender bias from embeddings.

研究 word embeddings 的可解释性，通过旋转 word spaces 进行 interpretable dimensions 的识别并保留原有信息，提出了 DensRay 方法进行 closed form 计算，相比于 Densifier 更加鲁棒，对 lexicon induction 和 word analogy 进行了实验，并展示了可解释性 word spaces 如何应用于去除嵌入中的性别偏见。