ViCLSR：一种结合自然语言推理的监督对比学习框架用于自然语言理解任务
ViCLSR: A Supervised Contrastive Learning Framework with Natural Language Inference for Natural Language Understanding Tasks
AI总结：
本文提出ViCLSR，一种针对越南语优化的监督对比学习框架，利用NLI数据集提升句子表示，优于PhoBERT等模型，在多个NLU任务中取得显著提升。
摘要：
高质量的文本表示对于自然语言理解（NLU）至关重要，但低资源语言如越南语因标注数据有限而面临挑战。尽管预训练模型如PhoBERT和CafeBERT表现良好，但其效果受限于数据稀缺。对比学习（CL）最近作为一种有前途的方法出现，能够改进句子表示，使模型有效区分语义相似和不相似的句子。我们提出ViCLSR（越南语对比学习用于句子表示），一种新的监督对比学习框架，专门优化越南语的句子嵌入，利用现有的自然语言推理（NLI）数据集。此外，我们提出一个过程来适应现有越南语数据集以进行监督学习，确保与CL方法兼容。我们的实验表明，ViCLSR在五个基准NLU数据集上显著优于强大的单语预训练模型PhoBERT，例如ViNLI（+6.97% F1）、ViWikiFC（+4.97% F1）、ViFactCheck（+9.02% F1）、UIT-ViCTSD（+5.36% F1）和ViMMRC2.0（+4.33% Accuracy）。ViCLSR表明，监督对比学习可以有效解决越南语NLU任务中的资源限制，并提高低资源语言的句子表示学习。此外，我们深入分析实验结果，以揭示对比学习模型优越性能的因素。ViCLSR已发布用于研究目的，以推动自然语言处理任务的发展。
英文摘要：
High-quality text representations are crucial for natural language understanding (NLU), but low-resource languages like Vietnamese face challenges due to limited annotated data. While pre-trained models like PhoBERT and CafeBERT perform well, their effectiveness is constrained by data scarcity. Contrastive learning (CL) has recently emerged as a promising approach for improving sentence representations, enabling models to effectively distinguish between semantically similar and dissimilar sentences. We propose ViCLSR (Vietnamese Contrastive Learning for Sentence Representations), a novel supervised contrastive learning framework specifically designed to optimize sentence embeddings for Vietnamese, leveraging existing natural language inference (NLI) datasets. Additionally, we propose a process to adapt existing Vietnamese datasets for supervised learning, ensuring compatibility with CL methods. Our experiments demonstrate that ViCLSR significantly outperforms the powerful monolingual pre-trained model PhoBERT on five benchmark NLU datasets such as ViNLI (+6.97% F1), ViWikiFC (+4.97% F1), ViFactCheck (+9.02% F1), UIT-ViCTSD (+5.36% F1), and ViMMRC2.0 (+4.33% Accuracy). ViCLSR shows that supervised contrastive learning can effectively address resource limitations in Vietnamese NLU tasks and improve sentence representation learning for low-resource languages. Furthermore, we conduct an in-depth analysis of the experimental results to uncover the factors contributing to the superior performance of contrastive learning models. ViCLSR is released for research purposes in advancing natural language processing tasks.
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