论文标题
SEMEVAL-2020任务1：通过在基于BERT的嵌入空间中聚类来进行语义移动跟踪的SST-BERT。
SST-BERT at SemEval-2020 Task 1: Semantic Shift Tracing by Clustering in BERT-based Embedding Spaces
论文作者
论文摘要
词汇语义变化检测（也称为语义转移跟踪）是识别随时间变化含义的单词的任务。无监督的语义转移追踪（Semeval2020的焦点）特别具有挑战性。考虑到无监督的设置，在这项工作中，我们建议确定每个目标词不同出现之间的簇，将其视为不同单词含义的代表。因此，在获得的群集中的分歧自然可以量化每个目标语言中每个目标单词的语义转移水平。为了利用这一想法，在文字出现的上下文化（基于BERT的）嵌入中进行聚类。获得的结果表明，我们的方法既表现良好（通过语言），又是整体上，我们超过所有提供的半厌恶基线。
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.
