论文标题
MultiSem在Semeval-2020任务3：词汇含义的微调BERT
MULTISEM at SemEval-2020 Task 3: Fine-tuning BERT for Lexical Meaning
论文作者
论文摘要
我们介绍提交给Semeval 2020任务3：上下文中的单词相似性（GWSC）中的多功能系统。我们通过对与GWSC相关的词汇语义任务进行微调，将语义知识注入预训练的BERT模型中。我们使用现有的语义注释数据集，并建议通过在上下文中自动生成的词汇替代品近似相似性。我们参与GWSC子任务，并介绍两种语言，英语和芬兰语。我们最好的英语车型在两个子任务的排名中占据了第三和第四位。对于相应的子任务中中列的芬兰模型的性能较低，突出了数据可用性在微调中的重要作用。
We present the MULTISEM systems submitted to SemEval 2020 Task 3: Graded Word Similarity in Context (GWSC). We experiment with injecting semantic knowledge into pre-trained BERT models through fine-tuning on lexical semantic tasks related to GWSC. We use existing semantically annotated datasets and propose to approximate similarity through automatically generated lexical substitutes in context. We participate in both GWSC subtasks and address two languages, English and Finnish. Our best English models occupy the third and fourth positions in the ranking for the two subtasks. Performance is lower for the Finnish models which are mid-ranked in the respective subtasks, highlighting the important role of data availability for fine-tuning.
