AVocaDo: Strategy for Adapting Vocabulary to Downstream Domain

• URL: http://arxiv.org/abs/2110.13434v1
• Date: Tue, 26 Oct 2021 06:26:01 GMT
• Title: AVocaDo: Strategy for Adapting Vocabulary to Downstream Domain
• Authors: Jimin Hong, Taehee Kim, Hyesu Lim and Jaegul Choo
• Abstract summary: We propose to consider the vocabulary as an optimizable parameter, allowing us to update the vocabulary by expanding it with domain-specific vocabulary. We preserve the embeddings of the added words from overfitting to downstream data by utilizing knowledge learned from a pretrained language model with a regularization term.
• Score: 17.115865763783336
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: During the fine-tuning phase of transfer learning, the pretrained vocabulary remains unchanged, while model parameters are updated. The vocabulary generated based on the pretrained data is suboptimal for downstream data when domain discrepancy exists. We propose to consider the vocabulary as an optimizable parameter, allowing us to update the vocabulary by expanding it with domain-specific vocabulary based on a tokenization statistic. Furthermore, we preserve the embeddings of the added words from overfitting to downstream data by utilizing knowledge learned from a pretrained language model with a regularization term. Our method achieved consistent performance improvements on diverse domains (i.e., biomedical, computer science, news, and reviews).

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