The success of Natural Language Understanding (NLU) benchmarks in various
languages, such as GLUE for English, CLUE for Chinese, KLUE for Korean, and
IndoNLU for Indonesian, has facilitated the evaluation of new NLU models across
a wide range of tasks. To establish a standardized set of benchmarks for
Vietnamese NLU, we introduce the first Vietnamese Language Understanding
Evaluation (VLUE) benchmark. The VLUE benchmark encompasses five datasets
covering different NLU tasks, including text classification, span extraction,
and natural language understanding. To provide an insightful overview of the
current state of Vietnamese NLU, we then evaluate seven state-of-the-art
pre-trained models, including both multilingual and Vietnamese monolingual
models, on our proposed VLUE benchmark. Furthermore, we present CafeBERT, a new
state-of-the-art pre-trained model that achieves superior results across all
tasks in the VLUE benchmark. Our model combines the proficiency of a
multilingual pre-trained model with Vietnamese linguistic knowledge. CafeBERT
is developed based on the XLM-RoBERTa model, with an additional pretraining
step utilizing a significant amount of Vietnamese textual data to enhance its
adaptation to the Vietnamese language. For the purpose of future research,
CafeBERT is made publicly available for research purposes.

为了评估新的自然语言理解模型在一系列任务上的表现，我们引入了第一个越南语语言理解评估（VLUE）基准，涵盖了五个不同的 NLU 任务，包括文本分类、跨度提取和自然语言理解。我们评估了七个最先进的预训练模型在我们提出的 VLUE 基准上的表现，包括多语言和越南语单语模型，并提出了 CafeBERT，一个在 VLUE 基准中所有任务上都取得优秀结果的最新预训练模型。