Ponwitayarat, W., Limkonchotiwat, P., Chuangsuwanich, E., & Nutanong, S. Space Decomposition for Sentence Embedding.
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Abstract
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A novel embedding space decomposition method utilizing a Mixture of Specialized Projectors, designed to distinguish and rank upper-range and lower-range samples accurately, and demonstrates that MixSP decreased the overlap representation between upper-range and lower-range classes significantly while outperforming competitors on STS and zero-shot benchmarks. 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.
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[Online; accessed 10. Jun. 2024]
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