Sentence Representations via Gaussian Embedding
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866929248960249856 |
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| author | Yoda, Shohei Tsukagoshi, Hayato Sasano, Ryohei Takeda, Koichi |
| author_facet | Yoda, Shohei Tsukagoshi, Hayato Sasano, Ryohei Takeda, Koichi |
| contents | Recent progress in sentence embedding, which represents the meaning of a sentence as a point in a vector space, has achieved high performance on tasks such as a semantic textual similarity (STS) task. However, sentence representations as a point in a vector space can express only a part of the diverse information that sentences have, such as asymmetrical relationships between sentences. This paper proposes GaussCSE, a Gaussian distribution-based contrastive learning framework for sentence embedding that can handle asymmetric relationships between sentences, along with a similarity measure for identifying inclusion relations. Our experiments show that GaussCSE achieves the same performance as previous methods in natural language inference tasks, and is able to estimate the direction of entailment relations, which is difficult with point representations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_12990 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Sentence Representations via Gaussian Embedding Yoda, Shohei Tsukagoshi, Hayato Sasano, Ryohei Takeda, Koichi Computation and Language Recent progress in sentence embedding, which represents the meaning of a sentence as a point in a vector space, has achieved high performance on tasks such as a semantic textual similarity (STS) task. However, sentence representations as a point in a vector space can express only a part of the diverse information that sentences have, such as asymmetrical relationships between sentences. This paper proposes GaussCSE, a Gaussian distribution-based contrastive learning framework for sentence embedding that can handle asymmetric relationships between sentences, along with a similarity measure for identifying inclusion relations. Our experiments show that GaussCSE achieves the same performance as previous methods in natural language inference tasks, and is able to estimate the direction of entailment relations, which is difficult with point representations. |
| title | Sentence Representations via Gaussian Embedding |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2305.12990 |