  In natural language processing (NLP), the semantic similarity task requires
large-scale, high-quality human-annotated labels for fine-tuning or evaluation.
By contrast, in cases of music similarity, such labels are expensive to collect
and largely dependent on the annotator's artistic preferences. Recent research
has demonstrated that embedding calibration technique can greatly increase
semantic similarity performance of the pre-trained language model without
fine-tuning. However, it is yet unknown which calibration method is the best
and how much performance improvement can be achieved. To address these issues,
we propose using composer information to construct labels for automatically
evaluating music similarity. Under this paradigm, we discover the optimal
combination of embedding calibration which achieves superior metrics than the
baseline methods.
