Improving Word Embedding Using Variational Dropout
Pre-trained word embeddings are essential in natural language processing (NLP). In recent years, many post-processing algorithms have been proposed to improve the pre-trained word embeddings. We present a novel method - Orthogonal Auto Encoder with Variational Dropout (OAEVD) for improving word embe...
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| Format: | Article |
| Language: | English |
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LibraryPress@UF
2023-05-01
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| Series: | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
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| Online Access: | https://journals.flvc.org/FLAIRS/article/view/133326 |
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| author | Zainab Albujasim Diana Inkpen Xuejun Han Yuhong Guo |
| author_facet | Zainab Albujasim Diana Inkpen Xuejun Han Yuhong Guo |
| author_sort | Zainab Albujasim |
| collection | DOAJ |
| description | Pre-trained word embeddings are essential in natural language processing (NLP). In recent years, many post-processing algorithms have been proposed to improve the pre-trained word embeddings. We present a novel method - Orthogonal Auto Encoder with Variational Dropout (OAEVD) for improving word embeddings based on orthogonal autoencoders and variational dropout. Specifically, the orthogonality constraint encourages more diversity in the latent space and increases semantic similarities between similar words, and variational dropout makes it more robust to overfitting. Empirical evaluation on a range of downstream NLP tasks, including semantic similarity, text classification, and concept categorization shows that our proposed method effectively improves the quality of pre-trained word embeddings. Moreover, the proposed method successfully reduces the dimensionality of pre-trained word embeddings while maintaining high performance. |
| format | Article |
| id | doaj-art-8c87ffa94e2e4e1f9c0485795cf87826 |
| institution | OA Journals |
| issn | 2334-0754 2334-0762 |
| language | English |
| publishDate | 2023-05-01 |
| publisher | LibraryPress@UF |
| record_format | Article |
| series | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| spelling | doaj-art-8c87ffa94e2e4e1f9c0485795cf878262025-08-20T01:52:22ZengLibraryPress@UFProceedings of the International Florida Artificial Intelligence Research Society Conference2334-07542334-07622023-05-013610.32473/flairs.36.13332669632Improving Word Embedding Using Variational DropoutZainab Albujasim0https://orcid.org/0009-0003-3569-6802Diana Inkpen1https://orcid.org/0000-0002-0202-2444Xuejun Han2https://orcid.org/0000-0001-5238-3291Yuhong Guo3https://orcid.org/0000-0002-7621-342XCarleton UniversityUniversity of OttawaCarleton UniversityCarleton UniversityPre-trained word embeddings are essential in natural language processing (NLP). In recent years, many post-processing algorithms have been proposed to improve the pre-trained word embeddings. We present a novel method - Orthogonal Auto Encoder with Variational Dropout (OAEVD) for improving word embeddings based on orthogonal autoencoders and variational dropout. Specifically, the orthogonality constraint encourages more diversity in the latent space and increases semantic similarities between similar words, and variational dropout makes it more robust to overfitting. Empirical evaluation on a range of downstream NLP tasks, including semantic similarity, text classification, and concept categorization shows that our proposed method effectively improves the quality of pre-trained word embeddings. Moreover, the proposed method successfully reduces the dimensionality of pre-trained word embeddings while maintaining high performance.https://journals.flvc.org/FLAIRS/article/view/133326word embeddingpost-processingvariational dropoutdimensionality reduction |
| spellingShingle | Zainab Albujasim Diana Inkpen Xuejun Han Yuhong Guo Improving Word Embedding Using Variational Dropout Proceedings of the International Florida Artificial Intelligence Research Society Conference word embedding post-processing variational dropout dimensionality reduction |
| title | Improving Word Embedding Using Variational Dropout |
| title_full | Improving Word Embedding Using Variational Dropout |
| title_fullStr | Improving Word Embedding Using Variational Dropout |
| title_full_unstemmed | Improving Word Embedding Using Variational Dropout |
| title_short | Improving Word Embedding Using Variational Dropout |
| title_sort | improving word embedding using variational dropout |
| topic | word embedding post-processing variational dropout dimensionality reduction |
| url | https://journals.flvc.org/FLAIRS/article/view/133326 |
| work_keys_str_mv | AT zainabalbujasim improvingwordembeddingusingvariationaldropout AT dianainkpen improvingwordembeddingusingvariationaldropout AT xuejunhan improvingwordembeddingusingvariationaldropout AT yuhongguo improvingwordembeddingusingvariationaldropout |