In this paper, we propose using deep neural networks to extract important
information from Vietnamese legal questions, a fundamental task towards
building a question answering system in the legal domain. Given a legal
question in natural language, the goal is to extract all the segments that
contain the needed information to answer the question. We introduce a deep
model that solves the task in three stages. First, our model leverages recent
advanced autoencoding language models to produce contextual word embeddings,
which are then combined with character-level and POS-tag information to form
word representations. Next, bidirectional long short-term memory networks are
employed to capture the relations among words and generate sentence-level
representations. At the third stage, borrowing ideas from graph-based
dependency parsing methods which provide a global view on the input sentence,
we use biaffine classifiers to estimate the probability of each pair of
start-end words to be an important segment. Experimental results on a public
Vietnamese legal dataset show that our model outperforms the previous work by a
large margin, achieving 94.79% in the F1 score. The results also prove the
effectiveness of using contextual features extracted from pre-trained language
models combined with other types of features such as character-level and
POS-tag features when training on a limited dataset.

本文提出使用深度神经网络从越南法律问题中提取重要信息，采用三个阶段的深度模型，利用高级自编码语言模型、字符级和 POS 标签信息生成词表示，Bidirectional LSTM 模型捕捉单词之间的关系并生成句子级别的表示，然后使用 biaffine 分类器来评估每对起始 - 结束单词成为重要细节的概率。

利用深度神经网络和双仿射分类器分析越南法律问题

Analyzing Vietnamese Legal Questions Using Deep Neural Networks with  Biaffine Classifiers

This paper builds off recent work from Kiperwasser & Goldberg (2016) using
neural attention in a simple graph-based dependency parser. We use a larger but
more thoroughly regularized parser than other recent BiLSTM-based approaches,
with biaffine classifiers to predict arcs and labels. Our parser gets state of
the art or near state of the art performance on standard treebanks for six
different languages, achieving 95.7% UAS and 94.1% LAS on the most popular
English PTB dataset. This makes it the highest-performing graph-based parser on
this benchmark---outperforming Kiperwasser Goldberg (2016) by 1.8% and
2.2%---and comparable to the highest performing transition-based parser
(Kuncoro et al., 2016), which achieves 95.8% UAS and 94.6% LAS. We also show
which hyperparameter choices had a significant effect on parsing accuracy,
allowing us to achieve large gains over other graph-based approaches.

本篇论文利用神经关注力和双仿射分类器，在较大规模的语法分析器上获得了最新的科技水平，并介绍了超参数选择对性能的显著影响。