Papers by TaeHee Kim
Unsupervised Neural Machine Translation for Low-Resource Domains via Meta-Learning (2021.acl-long)
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| Challenge: | Unsupervised machine translation suffers from data-scarce domains, authors report . a meta-learning algorithm trains the model to adapt to another domain by utilizing only a small amount of training data. |
| Approach: | They propose a meta-learning algorithm that trains the model to adapt to another domain . their model surpasses a transfer learning-based approach by up to 2-3 BLEU scores . |
| Outcome: | The proposed algorithm outperforms a transfer learning-based approach by 2-3 BLEU scores . the proposed model outperformed previous models in the domain of unsupervised machine translation . |
AVocaDo: Strategy for Adapting Vocabulary to Downstream Domain (2021.emnlp-main)
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| Challenge: | Existing methods to fine-tune a language model with a large corpus in a general domain are suboptimal for downstream data when domain discrepancy exists. |
| Approach: | They propose to consider the pretrained vocabulary as an optimizable parameter . they add domain specific vocabulary based on a tokenization statistic . their method achieved consistent performance improvements on diverse domains . |
| Outcome: | The proposed method achieves consistent performance improvements on diverse domains. |
Reweighting Strategy Based on Synthetic Data Identification for Sentence Similarity (2022.coling-1)
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| Challenge: | obtaining large amounts of human-annotated datasets to train a sentence embedding model is difficult and expensive. |
| Approach: | They propose to train a classifier that identifies machine-written sentences and then use it to train an embedding model on synthetic data. |
| Outcome: | The proposed method outperforms baselines on four real-world datasets and generalizes well. |