Detecting temporal semantic changes of words is an important task for various
NLP applications that must make time-sensitive predictions. Lexical Semantic
Change Detection (SCD) task considers the problem of predicting whether a given
target word, $w$, changes its meaning between two different text corpora, $C_1$
and $C_2$. For this purpose, we propose a supervised two-staged SCD method that
uses existing Word-in-Context (WiC) datasets. In the first stage, for a target
word $w$, we learn two sense-aware encoder that represents the meaning of $w$
in a given sentence selected from a corpus. Next, in the second stage, we learn
a sense-aware distance metric that compares the semantic representations of a
target word across all of its occurrences in $C_1$ and $C_2$. Experimental
results on multiple benchmark datasets for SCD show that our proposed method
consistently outperforms all previously proposed SCD methods for multiple
languages, establishing a novel state-of-the-art for SCD. Interestingly, our
findings imply that there are specialised dimensions that carry information
related to semantic changes of words in the sense-aware embedding space. Source
code is available at this https URL .

通过使用已有的 Word-in-Context（WiC）数据集，我们提出了一种监督式的两阶段语义变化检测（SCD）方法，该方法可预测给定目标词在两个不同文本语料库中是否改变其意义。实验证明，我们的方法在多个语言的多个基准数据集上始终优于以前提出的 SCD 方法，为 SCD 领域建立了新的最先进技术。有趣的是，我们的研究结果暗示，存在着与语义变化相关的专门维度，在意义感知嵌入空间中承载信息。