The rapid increase in civil disputes and the
limited capacity of legal systems have challenged the effectiveness of
traditional dispute resolution methods. Online Dispute Resolution (ODR)
platforms—such as China’s Internet Court, British Columbia’s Civil Resolution
Tribunal, and the UK’s Online Court—have emerged as promising solutions. A core
component of these platforms is the Classification of Online Consultation (COC),
which helps route civil legal questions to the appropriate departments.
However, manual classification is inefficient and error-prone, especially as
civil disputes become more diverse and complex.
COC tasks rely heavily on text classification, but several issues hinder
accurate results. Many platforms lack sufficient and balanced training data,
while the short, colloquial, and vague nature of users’ input makes it
difficult for traditional machine learning models to perform well.
Additionally, the use of Chinese text introduces further complexity due to
limited labeled data and grammatical challenges. These factors collectively
result in poor classification accuracy and hinder the effectiveness of civil
dispute resolution services online.
To address these challenges, the study introduces a deep transfer
learning-based classification method called CMDTL (Cross-platform Mapping with
Deep Transfer Learning). By transferring knowledge from richer data sources and
applying advanced techniques like joint distribution adaptation and improved
marginal Fisher analysis, this method significantly improves accuracy despite
limited and unbalanced data. It also uses ontology modeling to clarify legal
concepts, ensuring a more accurate understanding of the user’s legal queries.
This approach ultimately aims to enhance the efficiency and precision of online
civil dispute consultations.
International Conference on Computer Vision
The International Research Awards on Computer Vision recognize groundbreaking contributions in the field of computer vision, honoring researchers, scientists and innovators whose work has significantly advanced the domain. This prestigious award highlights excellence in fundamental theories, novel algorithms and real-world applications, fostering progress in artificial intelligence, image processing and deep learning.
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