This study deals with the problem of information retrieval (IR) for
Vietnamese legal texts. Despite being well researched in many languages,
information retrieval has still not received much attention from the Vietnamese
research community. This is especially true for the case of legal documents,
which are hard to process. This study proposes a new approach for information
retrieval for Vietnamese legal documents using sentence-transformer. Besides,
various experiments are conducted to make comparisons between different
transformer models, ranking scores, syllable-level, and word-level training.
The experiment results show that the proposed model outperforms models used in
current research on information retrieval for Vietnamese documents.

本研究使用句子转换器提出了一种新的越南法律文件信息检索方法，并通过多次实验比较不同的转换器模型、排名得分、音节层和单词层的训练，实验结果表明所提出的模型的表现优于当前越南文件信息检索的模型。

越南法律文本的多阶段信息检索

Multi-stage Information Retrieval for Vietnamese Legal Texts

Modern search systems use several large ranker models with transformer
architectures. These models require large computational resources and are not
suitable for usage on devices with limited computational resources. Knowledge
distillation is a popular compression technique that can reduce the resource
needs of such models, where a large teacher model transfers knowledge to a
small student model. To drastically reduce memory requirements and energy
consumption, we propose two extensions for a popular sentence-transformer
distillation procedure: generation of an optimal size vocabulary and
dimensionality reduction of the embedding dimension of teachers prior to
distillation. We evaluate these extensions on two different types of ranker
models. This results in extremely compressed student models whose analysis on a
test dataset shows the significance and utility of our proposed extensions.

本篇研究提出了在两种不同类型的 ranker 模型上扩展句子转换蒸馏过程的两种方法，包括生成最优尺寸的词汇表和在蒸馏之前对教师的嵌入维数进行降维。结果表明，采用这些扩展技术的学生模型具有极大的压缩程度，而且在测试数据集上表现出高度的有效性和能用性。