论文标题
cl-ims @ ducr-ita：volente o nolente：伯特在语义变化检测方面的表现不佳
CL-IMS @ DIACR-Ita: Volente o Nolente: BERT does not outperform SGNS on Semantic Change Detection
论文作者
论文摘要
我们介绍了参与diacr-ita共享的任务的结果，该任务涉及意大利语的词法语义变化检测。我们利用基于令牌的BERT嵌入时间点的平均成对距离在官方排名中以$ 0.72 $的准确性。当我们在Semeval-2020任务1的英语数据集上调整参数并达到高性能，但这并不能转化为意大利语Diacr-Ita数据集。我们的结果表明，我们没有设法找到强大的方法来利用词汇语义变化检测中的BERT嵌入。
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.
