The development of Natural Language Processing (NLI) datasets and models has been inspired by innovations in annotation design. With the rapid development of machine learning models today, the performance of existing machine learning models has quickly reached state-of-the-art results on a variety of tasks related to natural language processing, including natural language inference tasks. By using a pre-trained model during the annotation process, it is possible to challenge current NLI models by having humans produce premise-hypothesis combinations that the machine model cannot correctly predict. To remain attractive and challenging in the research of natural language inference for Vietnamese, in this paper, we introduce the adversarial NLI dataset to the NLP research community with the name ViANLI. This data set contains more than 10K premise-hypothesis pairs and is built by a continuously adjusting process to obtain the most out of the patterns generated by the annotators. ViANLI dataset has brought many difficulties to many current SOTA models when the accuracy of the most powerful model on the test set only reached 48.4%. Additionally, the experimental results show that the models trained on our dataset have significantly improved the results on other Vietnamese NLI datasets.

翻译：自然语言推理（NLI）数据集与模型的发展一直受到标注设计创新的推动。随着当今机器学习模型的快速发展，现有机器学习模型在包括自然语言推理任务在内的各种自然语言处理相关任务上的性能已迅速达到最先进水平。通过在标注过程中使用预训练模型，可以让人工生成机器学习模型无法正确预测的前提-假设组合，从而对当前NLI模型构成挑战。为使越南语自然语言推理研究保持吸引力与挑战性，本文向NLP研究社区引入了名为ViANLI的对抗性NLI数据集。该数据集包含超过1万个前提-假设对，并通过持续调整的构建流程，以充分利用标注者生成的模式。ViANLI数据集对当前众多SOTA模型造成了显著困难，其中性能最强模型在测试集上的准确率仅达到48.4%。此外，实验结果表明，基于本数据集训练的模型在其他越南语NLI数据集上的结果均有显著提升。