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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2309.08325 |
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| _version_ | 1866929240010653696 |
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| author | Lo, Chun Hei Lam, Wai Cheng, Hong Emerson, Guy |
| author_facet | Lo, Chun Hei Lam, Wai Cheng, Hong Emerson, Guy |
| contents | Functional Distributional Semantics (FDS) models the meaning of words by truth-conditional functions. This provides a natural representation for hypernymy but no guarantee that it can be learnt when FDS models are trained on a corpus. In this paper, we probe into FDS models and study the representations learnt, drawing connections between quantifications, the Distributional Inclusion Hypothesis (DIH), and the variational-autoencoding objective of FDS model training. Using synthetic data sets, we reveal that FDS models learn hypernymy on a restricted class of corpus that strictly follows the DIH. We further introduce a training objective that both enables hypernymy learning under the reverse of the DIH and improves hypernymy detection from real corpora. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_08325 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Distributional Inclusion Hypothesis and Quantifications: Probing for Hypernymy in Functional Distributional Semantics Lo, Chun Hei Lam, Wai Cheng, Hong Emerson, Guy Computation and Language Functional Distributional Semantics (FDS) models the meaning of words by truth-conditional functions. This provides a natural representation for hypernymy but no guarantee that it can be learnt when FDS models are trained on a corpus. In this paper, we probe into FDS models and study the representations learnt, drawing connections between quantifications, the Distributional Inclusion Hypothesis (DIH), and the variational-autoencoding objective of FDS model training. Using synthetic data sets, we reveal that FDS models learn hypernymy on a restricted class of corpus that strictly follows the DIH. We further introduce a training objective that both enables hypernymy learning under the reverse of the DIH and improves hypernymy detection from real corpora. |
| title | Distributional Inclusion Hypothesis and Quantifications: Probing for Hypernymy in Functional Distributional Semantics |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2309.08325 |