  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 involves 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 encoders that represent 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
achieves strong performance in multiple languages. Additionally, our method
achieves significant improvements on WiC benchmarks compared to a sense-aware
encoder with conventional distance functions. Source code is available at
https://github.com/LivNLP/svp-sdml .
