Lexical semantic change detection (also known as semantic shift tracing) is a
task of identifying words that have changed their meaning over time.
Unsupervised semantic shift tracing, focal point of SemEval2020, is
particularly challenging. Given the unsupervised setup, in this work, we
propose to identify clusters among different occurrences of each target word,
considering these as representatives of different word meanings. As such,
disagreements in obtained clusters naturally allow to quantify the level of
semantic shift per each target word in four target languages. To leverage this
idea, clustering is performed on contextualized (BERT-based) embeddings of word
occurrences. The obtained results show that our approach performs well both
measured separately (per language) and overall, where we surpass all provided
SemEval baselines.

本文讨论了关于词汇语义变化检测的方法，并提出了使用基于 BERT 的上下文化嵌入来对每个目标词的不同出现之间进行聚类，从而量化每个目标词的语义变化水平的方法，并在四种目标语言中取得了良好的表现，超过了所有提供的 SemEval 基线。