The representation degeneration problem in Contextual Word Representations
(CWRs) hurts the expressiveness of the embedding space by forming an
anisotropic cone where even unrelated words have excessively positive
correlations. Existing techniques for tackling this issue require a learning
process to re-train models with additional objectives and mostly employ a
global assessment to study isotropy. Our quantitative analysis over isotropy
shows that a local assessment could be more accurate due to the clustered
structure of CWRs. Based on this observation, we propose a local cluster-based
method to address the degeneration issue in contextual embedding spaces. We
show that in clusters including punctuations and stop words, local dominant
directions encode structural information, removing which can improve CWRs
performance on semantic tasks. Moreover, we find that tense information in verb
representations dominates sense semantics. We show that removing dominant
directions of verb representations can transform the space to better suit
semantic applications. Our experiments demonstrate that the proposed
cluster-based method can mitigate the degeneration problem on multiple tasks.

本文提出了一种基于局部聚类的方法来应对上下文嵌入空间中的退化问题，实验证明该方法可以在多个任务上缓解该问题。