  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.
