  We apply contextualised word embeddings to lexical semantic change detection
in the SemEval-2020 Shared Task 1. This paper focuses on Subtask 2, ranking
words by the degree of their semantic drift over time. We analyse the
performance of two contextualising architectures (BERT and ELMo) and three
change detection algorithms. We find that the most effective algorithms rely on
the cosine similarity between averaged token embeddings and the pairwise
distances between token embeddings. They outperform strong baselines by a large
margin (in the post-evaluation phase, we have the best Subtask 2 submission for
SemEval-2020 Task 1), but interestingly, the choice of a particular algorithm
depends on the distribution of gold scores in the test set.
