  We present the results of our participation in the DIACR-Ita shared task on
lexical semantic change detection for Italian. We exploit Average Pairwise
Distance of token-based BERT embeddings between time points and rank 5 (of 8)
in the official ranking with an accuracy of $.72$. While we tune parameters on
the English data set of SemEval-2020 Task 1 and reach high performance, this
does not translate to the Italian DIACR-Ita data set. Our results show that we
do not manage to find robust ways to exploit BERT embeddings in lexical
semantic change detection.
