AVocaDo: Strategy for Adapting Vocabulary to Downstream Domain
Jimin Hong, TaeHee Kim, Hyesu Lim, Jaegul Choo
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).
BibTeX
@inproceedings{hong-etal-2021-avocado,
title = "{AV}oca{D}o: Strategy for Adapting Vocabulary to Downstream Domain",
author = "Hong, Jimin and
Kim, TaeHee and
Lim, Hyesu and
Choo, Jaegul",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.385/",
doi = "10.18653/v1/2021.emnlp-main.385",
pages = "4692--4700"
}