Space Decomposition for Sentence Embedding
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866914824829534208 |
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| author | Ponwitayarat, Wuttikorn Limkonchotiwat, Peerat Chuangsuwanich, Ekapol Nutanong, Sarana |
| author_facet | Ponwitayarat, Wuttikorn Limkonchotiwat, Peerat Chuangsuwanich, Ekapol Nutanong, Sarana |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2406_03125 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Space Decomposition for Sentence Embedding Ponwitayarat, Wuttikorn Limkonchotiwat, Peerat Chuangsuwanich, Ekapol Nutanong, Sarana Computation and Language 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. |
| title | Space Decomposition for Sentence Embedding |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2406.03125 |