2,412 research outputs found
Learning to Predict Charges for Criminal Cases with Legal Basis
The charge prediction task is to determine appropriate charges for a given
case, which is helpful for legal assistant systems where the user input is fact
description. We argue that relevant law articles play an important role in this
task, and therefore propose an attention-based neural network method to jointly
model the charge prediction task and the relevant article extraction task in a
unified framework. The experimental results show that, besides providing legal
basis, the relevant articles can also clearly improve the charge prediction
results, and our full model can effectively predict appropriate charges for
cases with different expression styles.Comment: 10 pages, accepted by EMNLP 201
Exploring the State of the Art in Legal QA Systems
Answering questions related to the legal domain is a complex task, primarily
due to the intricate nature and diverse range of legal document systems.
Providing an accurate answer to a legal query typically necessitates
specialized knowledge in the relevant domain, which makes this task all the
more challenging, even for human experts. QA (Question answering systems) are
designed to generate answers to questions asked in human languages. They use
natural language processing to understand questions and search through
information to find relevant answers. QA has various practical applications,
including customer service, education, research, and cross-lingual
communication. However, they face challenges such as improving natural language
understanding and handling complex and ambiguous questions. Answering questions
related to the legal domain is a complex task, primarily due to the intricate
nature and diverse range of legal document systems. Providing an accurate
answer to a legal query typically necessitates specialized knowledge in the
relevant domain, which makes this task all the more challenging, even for human
experts. At this time, there is a lack of surveys that discuss legal question
answering. To address this problem, we provide a comprehensive survey that
reviews 14 benchmark datasets for question-answering in the legal field as well
as presents a comprehensive review of the state-of-the-art Legal Question
Answering deep learning models. We cover the different architectures and
techniques used in these studies and the performance and limitations of these
models. Moreover, we have established a public GitHub repository where we
regularly upload the most recent articles, open data, and source code. The
repository is available at:
\url{https://github.com/abdoelsayed2016/Legal-Question-Answering-Review}
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