We introduce efficient deep learning-based methods for legal document
processing including Legal Document Retrieval and Legal Question Answering
tasks in the Automated Legal Question Answering Competition (ALQAC 2022). In
this competition, we achieve 1\textsuperscript{st} place in the first task and
3\textsuperscript{rd} place in the second task. Our method is based on the
XLM-RoBERTa model that is pre-trained from a large amount of unlabeled corpus
before fine-tuning to the specific tasks. The experimental results showed that
our method works well in legal retrieval information tasks with limited labeled
data. Besides, this method can be applied to other information retrieval tasks
in low-resource languages.

本文介绍了基于深度学习的法律文件处理方法，包括自动法律问答竞赛（ALQAC 2022）的法律文件检索和法律问答任务。使用 XLM-RoBERTa 模型在有限的标记数据下表现良好，在低资源语言的其他信息检索任务中也可以应用。

Miko Team: ALQAC 2022 中的法律问答深度学习方法

Miko Team: Deep Learning Approach for Legal Question Answering in ALQAC 2022

Legal documents are unstructured, use legal jargon, and have considerable
length, making them difficult to process automatically via conventional text
processing techniques. A legal document processing system would benefit
substantially if the documents could be segmented into coherent information
units. This paper proposes a new corpus of legal documents annotated (with the
help of legal experts) with a set of 13 semantically coherent units labels
(referred to as Rhetorical Roles), e.g., facts, arguments, statute, issue,
precedent, ruling, and ratio. We perform a thorough analysis of the corpus and
the annotations. For automatically segmenting the legal documents, we
experiment with the task of rhetorical role prediction: given a document,
predict the text segments corresponding to various roles. Using the created
corpus, we experiment extensively with various deep learning-based baseline
models for the task. Further, we develop a multitask learning (MTL) based deep
model with document rhetorical role label shift as an auxiliary task for
segmenting a legal document. The proposed model shows superior performance over
the existing models. We also experiment with model performance in the case of
domain transfer and model distillation techniques to see the model performance
in limited data conditions.

本文提出了一种新的法律文件语料库，标注了 13 个语义连贯性单元标签（称为修辞角色），并使用该语料库进行了深度学习模型，其中包括多任务学习的实验，以自动处理法律文件并将其分成相关信息单元。