  One of the advantages of using natural language processing (NLP) technology
for music is to fully exploit the embedding based representation learning
paradigm that can easily handle classical tasks such as semantic similarity.
However, recent researches have revealed the poor performance issue of common
baseline methods for semantic similarity in NLP. They show that some simple
embedding calibration methods can easily promote the performance of semantic
similarity without extra training hence is ready-to-use. Nevertheless, it is
still unclear which is the best combination of calibration methods and by how
much can we further improve the performance with such methods. Most
importantly, previous works are based on auto-encoder Transformer, hence the
performance under auto-regressive model for music is unclear. These render the
following open questions: does embedding based semantic similarity also apply
for auto-regressive music model, does poor baseline issue for semantic
similarity also exists, and if so, are there unexplored embedding calibration
methods to better promote the performance of music semantic similarity? In this
paper, we answer these questions by exploring different combination of
embedding calibration under auto-regressive language model for symbolic music.
Our results show that music semantic similarity works under auto-regressive
model, and also suffers from poor baseline issues like in NLP. Furthermore, we
provide optimal combination of embedding calibration that has not been explored
in previous researches. Results show that such combination of embedding
calibration can greatly improve music semantic similarity without further
training tasks.
