  Determining sentence pair similarity is crucial for various NLP tasks. A
common technique to address this is typically evaluated on a continuous
semantic textual similarity scale from 0 to 5. However, based on a linguistic
observation in STS annotation guidelines, we found that the score in the range
[4,5] indicates an upper-range sample, while the rest are lower-range samples.
This necessitates a new approach to treating the upper-range and lower-range
classes separately. In this paper, we introduce a novel embedding space
decomposition method called MixSP utilizing a Mixture of Specialized
Projectors, designed to distinguish and rank upper-range and lower-range
samples accurately. The experimental results demonstrate that MixSP decreased
the overlap representation between upper-range and lower-range classes
significantly while outperforming competitors on STS and zero-shot benchmarks.
