Abstract
Natural language inference models are important resources for many natural language understanding applications. These models are possibly built by training or fine-tuning using deep neural network architectures for state-of-the-art results. This means high-quality annotated datasets are important for building state-of-the-art models. Therefore, we propose a method of building Vietnamese dataset for training Vietnamese inference models which work on native Vietnamese texts. Our method aims at two issues: removing cue marks and ensuring the writing-style of Vietnamese texts. If a dataset contains cue marks, the trained models will identify the relation between a premise and a hypothesis without semantic computation. For evaluation, we fine-tuned a BERT model on our dataset and compared it to a BERT model which was fine-tuned on XNLI dataset. The model which was fine-tuned on our dataset has the accuracy of 86.05% while the other has the accuracy of 64.04% when testing on our Vietnamese test set. This means our method is possibly used for building a high-quality Vietnamese natural language inference dataset.
Keywords
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Nguyen, C.T., Nguyen, D.T. (2021). Building a Vietnamese Dataset for Natural Language Inference Models. In: Dang, T.K., Küng, J., Chung, T.M., Takizawa, M. (eds) Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. FDSE 2021. Communications in Computer and Information Science, vol 1500. Springer, Singapore. https://doi.org/10.1007/978-981-16-8062-5_12
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