Papers by Seungwoo Choi
BridG MT: Enhancing LLMs’ Machine Translation Capabilities with Sentence Bridging and Gradual MT (2025.findings-acl)
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| Challenge: | Recent Large Language Models (LLMs) have demonstrated impressive translation performance without the need for fine-tuning on additional parallel corpora. |
| Approach: | They propose a method that combines Sentence Bridging and Gradual MT to enhance LLMs' translation performance even outperforming translation methods that rely on a large number of few-shot examples. |
| Outcome: | The proposed method outperforms translation methods that rely on a large number of few-shot examples even when the source and target languages are low-resource languages. |
Do Language Models Understand Measurements? (2022.findings-emnlp)
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| Challenge: | Existing studies on numerical reasoning over text (NRoT) tests PLMs to understand numbers in contexts where numbers are an integral part of the context. |
| Approach: | They propose a simple embedding strategy to better distinguish between numbers and units, which leads to a significant improvement in probing tasks. |
| Outcome: | The proposed model distinguishes between numbers and units, which leads to significant improvement in probing tasks. |
Rethinking Style Transformer with Energy-based Interpretation: Adversarial Unsupervised Style Transfer using a Pretrained Model (2022.emnlp-main)
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Hojun Cho, Dohee Kim, Seungwoo Ryu, ChaeHun Park, Hyungjong Noh, Jeong-in Hwang, Minseok Choi, Edward Choi, Jaegul Choo
| Challenge: | Existing methods to train text style transfer models with adversarial loss degrade fluency compared to other metrics. |
| Approach: | They propose a method which leverages a pretrained language model to improve fluency by restructuring the discriminator and the model itself. |
| Outcome: | The proposed model achieves state-of-the-art on three public benchmarks and achieved state-outperformance on the overall metrics. |