ConCSE: Unified Contrastive Learning and Augmentation for Code-Switched Embeddings

• URL: http://arxiv.org/abs/2409.00120v1
• Date: Wed, 28 Aug 2024 11:27:21 GMT
• Title: ConCSE: Unified Contrastive Learning and Augmentation for Code-Switched Embeddings
• Authors: Jangyeong Jeon, Sangyeon Cho, Minuk Ma, Junyoung Kim,
• Abstract summary: This paper examines the Code-Switching (CS) phenomenon where two languages intertwine within a single utterance. We highlight that the current Equivalence Constraint (EC) theory for CS in other languages may only partially capture English-Korean CS complexities. We introduce a novel Koglish dataset tailored for English-Korean CS scenarios to mitigate such challenges.
• Score: 4.68732641979009
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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