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We apply contextualised word embeddings to lexical semantic change detectionin the SemEval-2020 Shared Task 1. This paper focuses on Subtask 2, rankingwords by the degree of their semantic drift over time. We analyse theperformance of two contextualising architectures (BERT and ELMo) and threechange detection algorithms. We find that the most effective algorithms rely onthe cosine similarity between averaged token embeddings and the pairwisedistances between token embeddings. They outperform strong baselines by a largemargin (in the post-evaluation phase, we have the best Subtask 2 submission forSemEval-2020 Task 1), but interestingly, the choice of a particular algorithmdepends on the distribution of gold scores in the test set.

UiO-UvA at SemEval-2020 Task 1: Contextualised Embeddings for Lexical Semantic Change Detection