Hyesu Lim


2023

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PRiSM: Enhancing Low-Resource Document-Level Relation Extraction with Relation-Aware Score Calibration
Minseok Choi | Hyesu Lim | Jaegul Choo
Findings of the Association for Computational Linguistics: IJCNLP-AACL 2023 (Findings)

2021

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AVocaDo: Strategy for Adapting Vocabulary to Downstream Domain
Jimin Hong | TaeHee Kim | Hyesu Lim | Jaegul Choo
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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).