Autoencoding Improves Pre-trained Word Embeddings

Kaneko, Masahiro and Bollegala, Danushka ORCID: 0000-0003-4476-7003
(2020) Autoencoding Improves Pre-trained Word Embeddings. In: Proceedings of the 28th International Conference on Computational Linguistics, 2020-12 - 2020-12, Virtual.

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Prior work investigating the geometry of pre-trained word embeddings have shown that word embeddings to be distributed in a narrow cone and by centering and projecting using principal component vectors one can increase the accuracy of a given set of pre-trained word embeddings. However, theoretically this post-processing step is equivalent to applying a linear autoencoder to minimise the squared `2 reconstruction error. This result contradicts prior work (Mu and Viswanath, 2018) that proposed to remove the top principal components from pre-trained embeddings. We experimentally verify our theoretical claims and show that retaining the top principal components is indeed useful for improving pre-trained word embeddings, without requiring access to additional linguistic resources or labeled data.

Item Type: Conference or Workshop Item (Unspecified)
Depositing User: Symplectic Admin
Date Deposited: 03 Nov 2020 10:26
Last Modified: 09 Jun 2024 03:15
DOI: 10.18653/v1/2020.coling-main.149
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