We present a qualitative analysis of the (potentially erroneous) outputs of
contextualized embedding-based methods for detecting diachronic semantic
change. First, we introduce an ensemble method outperforming previously
described contextualized approaches. This method is used as a basis for an
in-depth analysis of the degrees of semantic change predicted for English words
across 5 decades. Our findings show that contextualized methods can often
predict high change scores for words which are not undergoing any real
diachronic semantic shift in the lexicographic sense of the term (or at least
the status of these shifts is questionable). Such challenging cases are
discussed in detail with examples, and their linguistic categorization is
proposed. Our conclusion is that pre-trained contextualized language models are
prone to confound changes in lexicographic senses and changes in contextual
variance, which naturally stem from their distributional nature, but is
different from the types of issues observed in methods based on static
embeddings. Additionally, they often merge together syntactic and semantic
aspects of lexical entities. We propose a range of possible future solutions to
these issues.

本文研究使用基于上下文嵌入方法进行检测历时语义变化的可能存在的输出错误。通过引入单一方法并进行深入的分析，作者发现这种方法可能会将词汇的词典含义变化与上下文语境的变化混淆，同时将词汇实体的句法和语义方面合并在一起。本文提出了一些解决这些问题的未来可能方案。