Neural Proto-Language Reconstruction
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911851676172288 |
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| author | Cui, Chenxuan Chen, Ying Wang, Qinxin Mortensen, David R. |
| author_facet | Cui, Chenxuan Chen, Ying Wang, Qinxin Mortensen, David R. |
| contents | Proto-form reconstruction has been a painstaking process for linguists. Recently, computational models such as RNN and Transformers have been proposed to automate this process. We take three different approaches to improve upon previous methods, including data augmentation to recover missing reflexes, adding a VAE structure to the Transformer model for proto-to-language prediction, and using a neural machine translation model for the reconstruction task. We find that with the additional VAE structure, the Transformer model has a better performance on the WikiHan dataset, and the data augmentation step stabilizes the training. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_15690 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Neural Proto-Language Reconstruction Cui, Chenxuan Chen, Ying Wang, Qinxin Mortensen, David R. Computation and Language Machine Learning Proto-form reconstruction has been a painstaking process for linguists. Recently, computational models such as RNN and Transformers have been proposed to automate this process. We take three different approaches to improve upon previous methods, including data augmentation to recover missing reflexes, adding a VAE structure to the Transformer model for proto-to-language prediction, and using a neural machine translation model for the reconstruction task. We find that with the additional VAE structure, the Transformer model has a better performance on the WikiHan dataset, and the data augmentation step stabilizes the training. |
| title | Neural Proto-Language Reconstruction |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2404.15690 |