  In this paper, we study the problem of legal domain adaptation problem from
an imbalanced source domain to a partial target domain. The task aims to
improve legal judgment predictions for non-professional fact descriptions. We
formulate this task as a partial-and-imbalanced domain adaptation problem.
Though deep domain adaptation has achieved cutting-edge performance in many
unsupervised domain adaptation tasks. However, due to the negative transfer of
samples in non-shared classes, it is hard for current domain adaptation model
to solve the partial-and-imbalanced transfer problem. In this work, we explore
large-scale non-shared but related classes data in the source domain with a
hierarchy weighting adaptation to tackle this limitation. We propose to embed a
novel pArtial Imbalanced Domain Adaptation technique (AIDA) in the deep
learning model, which can jointly borrow sibling knowledge from non-shared
classes to shared classes in the source domain and further transfer the shared
classes knowledge from the source domain to the target domain. Experimental
results show that our model outperforms the state-of-the-art algorithms.
