  The use of language is subject to variation over time as well as across
social groups and knowledge domains, leading to differences even in the
monolingual scenario. Such variation in word usage is often called lexical
semantic change (LSC). The goal of LSC is to characterize and quantify language
variations with respect to word meaning, to measure how distinct two language
sources are (that is, people or language models). Because there is hardly any
data available for such a task, most solutions involve unsupervised methods to
align two embeddings and predict semantic change with respect to a distance
measure. To that end, we propose a self-supervised approach to model lexical
semantic change by generating training samples by introducing perturbations of
word vectors in the input corpora. We show that our method can be used for the
detection of semantic change with any alignment method. Furthermore, it can be
used to choose the landmark words to use in alignment and can lead to
substantial improvements over the existing techniques for alignment.
  We illustrate the utility of our techniques using experimental results on
three different datasets, involving words with the same or different meanings.
Our methods not only provide significant improvements but also can lead to
novel findings for the LSC problem.
