The representation space built by pretrained Language Models (LMs) encodes
rich information about words and their relationships (e.g., similarity,
hypernymy/hyponymy, polysemy) as well as abstract semantic notions (e.g.,
intensity). In this paper, we demonstrate that lexical stylistic notions such
as complexity, formality, and figurativeness, can also be identified in this
space. We show that it is possible to derive a vector representation for each
of these stylistic notions, from only a small number of seed text pairs. Using
these vectors, we can characterize new texts in terms of these dimensions using
simple calculations in the corresponding embedding space. We perform
experiments on five datasets and find that static embeddings encode these
features more accurately at the level of words and phrases, whereas
contextualized LMs perform better on longer texts. The lower performance of
contextualized representations at the word level is partially attributable to
the anisotropy of their vector space, which can be corrected through techniques
like standardization to further improve performance.

本研究发现，通过预训练语言模型（LMs）建立的表征空间，不仅编码了单词及其关系等抽象语义概念，也能够识别如复杂性、正式程度和象征性等字词风格，以及使用在小数量文本中产生的矢量表征。此外，本文还对这些表征空间的词级与短语级特征进行了分析，并表明无论静态嵌入还是上下文化 LMs，都可以在对不同长度的文本进行字符化时得到更高的准确度。