Space Decomposition for Sentence Embedding
<|reference_start|>Space Decomposition for Sentence Embedding: 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.<|reference_end|>
@article{ponwitayarat2024space,
    title={Space Decomposition for Sentence Embedding},
    author={Wuttikorn Ponwitayarat, Peerat Limkonchotiwat, Ekapol Chuangsuwanich,
  Sarana Nutanong},
    journal={arXiv preprint arXiv:2406.03125},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.03125},
    primaryClass={cs.CL}
}