Existing Vietnamese Natural Language Inference (NLI) datasets lack adversarial complexity, limiting their ability to evaluate model robustness against challenging linguistic phenomena. In this article, we address the gap in robust Vietnamese NLI resources by introducing ViANLI, the first adversarial NLI dataset for Vietnamese, and propose NLIMoE, a Mixture-of-Experts model to tackle its complexity. We construct ViANLI using an adversarial human-and-machine-in-the-loop approach with rigorous verification. NLIMoE integrates expert subnetworks with a learned dynamic routing mechanism on top of a shared transformer encoder. ViANLI comprises over 10,000 premise-hypothesis pairs and challenges state-of-the-art models, with XLM-R Large achieving only 45.5% accuracy, while NLIMoE reaches 47.3%. Training with ViANLI improves performance on other benchmark Vietnamese NLI datasets including ViNLI, VLSP2021-NLI, and VnNewsNLI. ViANLI is released for enhancing research into model robustness and enriching resources for future Vietnamese and multilingual NLI research.

翻译：现有的越南语自然语言推理数据集缺乏对抗性复杂度，限制了其评估模型应对挑战性语言现象鲁棒性的能力。本文通过引入首个越南语对抗性自然语言推理数据集ViANLI，并提出应对其复杂性的专家混合模型NLIMoE，以填补越南语鲁棒性自然语言推理资源的空白。我们采用严格验证的人机协同对抗方法构建ViANLI数据集。NLIMoE在共享Transformer编码器基础上，通过学习的动态路由机制集成专家子网络。ViANLI包含超过10,000个前提-假设对，对现有最优模型构成显著挑战：XLM-R Large模型仅获得45.5%的准确率，而NLIMoE达到47.3%。使用ViANLI进行训练能提升模型在ViNLI、VLSP2021-NLI和VnNewsNLI等其他越南语自然语言推理基准数据集上的性能。ViANLI的发布将推动模型鲁棒性研究，并为未来越南语及多语言自然语言推理研究提供丰富资源。