Papers by Woojin Chung

2 papers
UniCoM: A Universal Code-Switching Speech Generator (2025.findings-emnlp)

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Challenge: Code-switching (CS) is a common phenomenon in real-world conversations and poses significant challenges for multilingual speech technology.
Approach: They propose a pipeline for generating high-quality, natural CS samples without altering sentence semantics.
Outcome: The proposed pipeline generates high-quality, natural CS samples without altering sentence semantics without alteration of sentence semantic.
Stable Language Model Pre-training by Reducing Embedding Variability (2024.emnlp-main)

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Challenge: Stable pre-training is essential for achieving better-performing language models, but tracking pre-train stability is impractical due to high computational costs.
Approach: They propose to use Token Embedding Variability as a proxy to estimate pre-training stability.
Outcome: The proposed method improves stability and lowers perplexities even at deeper layer counts.

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