70,393 research outputs found
SECaps: A Sequence Enhanced Capsule Model for Charge Prediction
Automatic charge prediction aims to predict appropriate final charges
according to the fact descriptions for a given criminal case. Automatic charge
prediction plays a critical role in assisting judges and lawyers to improve the
efficiency of legal decisions, and thus has received much attention.
Nevertheless, most existing works on automatic charge prediction perform
adequately on high-frequency charges but are not yet capable of predicting
few-shot charges with limited cases. In this paper, we propose a Sequence
Enhanced Capsule model, dubbed as SECaps model, to relieve this problem.
Specifically, following the work of capsule networks, we propose the seq-caps
layer, which considers sequence information and spatial information of legal
texts simultaneously. Then we design a attention residual unit, which provides
auxiliary information for charge prediction. In addition, our SECaps model
introduces focal loss, which relieves the problem of imbalanced charges.
Comparing the state-of-the-art methods, our SECaps model obtains 4.5% and 6.4%
absolutely considerable improvements under Macro F1 in Criminal-S and
Criminal-L respectively. The experimental results consistently demonstrate the
superiorities and competitiveness of our proposed model.Comment: 13 pages, 3figures, 5 table
Graph Few-shot Learning via Knowledge Transfer
Towards the challenging problem of semi-supervised node classification, there
have been extensive studies. As a frontier, Graph Neural Networks (GNNs) have
aroused great interest recently, which update the representation of each node
by aggregating information of its neighbors. However, most GNNs have shallow
layers with a limited receptive field and may not achieve satisfactory
performance especially when the number of labeled nodes is quite small. To
address this challenge, we innovatively propose a graph few-shot learning (GFL)
algorithm that incorporates prior knowledge learned from auxiliary graphs to
improve classification accuracy on the target graph. Specifically, a
transferable metric space characterized by a node embedding and a
graph-specific prototype embedding function is shared between auxiliary graphs
and the target, facilitating the transfer of structural knowledge. Extensive
experiments and ablation studies on four real-world graph datasets demonstrate
the effectiveness of our proposed model.Comment: Full paper (with Appendix) of AAAI 202
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