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.

翻译：我们介绍了我们参加意大利语语义变化词汇学探测的DIACR-Ita共同任务的结果。我们利用时间点与官方排名第5级(8级中的)之间象征性BERT嵌入的平均对称距离,精确度为72美元。虽然我们为SemEval-2020任务1的英国数据集调制参数并达到很高的性能,但这并没有转化为意大利语的DIACR-Ita数据集。我们的结果显示,我们无法找到强有力的方法在词汇学术语变化探测中利用BERT嵌入。