ConCSE: Unified Contrastive Learning and Augmentation for Code-Switched Embeddings
<|reference_start|>ConCSE: Unified Contrastive Learning and Augmentation for Code-Switched Embeddings: This paper examines the Code-Switching (CS) phenomenon where two languages intertwine within a single utterance. There exists a noticeable need for research on the CS between English and Korean. We highlight that the current Equivalence Constraint (EC) theory for CS in other languages may only partially capture English-Korean CS complexities due to the intrinsic grammatical differences between the languages. We introduce a novel Koglish dataset tailored for English-Korean CS scenarios to mitigate such challenges. First, we constructed the Koglish-GLUE dataset to demonstrate the importance and need for CS datasets in various tasks. We found the differential outcomes of various foundation multilingual language models when trained on a monolingual versus a CS dataset. Motivated by this, we hypothesized that SimCSE, which has shown strengths in monolingual sentence embedding, would have limitations in CS scenarios. We construct a novel Koglish-NLI (Natural Language Inference) dataset using a CS augmentation-based approach to verify this. From this CS-augmented dataset Koglish-NLI, we propose a unified contrastive learning and augmentation method for code-switched embeddings, ConCSE, highlighting the semantics of CS sentences. Experimental results validate the proposed ConCSE with an average performance enhancement of 1.77\% on the Koglish-STS(Semantic Textual Similarity) tasks.<|reference_end|>
@article{jeon2024concse:,
    title={ConCSE: Unified Contrastive Learning and Augmentation for Code-Switched
  Embeddings},
    author={Jangyeong Jeon, Sangyeon Cho, Minuk Ma, and Junyoung Kim},
    journal={arXiv preprint arXiv:2409.00120},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.00120},
    primaryClass={cs.CL cs.AI}
}