  Semantic change detection concerns the task of identifying words whose
meaning has changed over time. The current state-of-the-art detects the level
of semantic change in a word by comparing its vector representation in two
distinct time periods, without considering its evolution through time. In this
work, we propose three variants of sequential models for detecting semantically
shifted words, effectively accounting for the changes in the word
representations over time, in a temporally sensitive manner. Through extensive
experimentation under various settings with both synthetic and real data we
showcase the importance of sequential modelling of word vectors through time
for detecting the words whose semantics have changed the most. Finally, we take
a step towards comparing different approaches in a quantitative manner,
demonstrating that the temporal modelling of word representations yields a
clear-cut advantage in performance.
