Abstract
Charge prediction aims to predict the final charge for a case according to its fact description and plays an important role in legal assistance systems. With deep learning based methods, prediction on high-frequency charges has achieved promising results but that on few-shot charges is still challenging. In this work, we propose a framework with multi-grained features and mutual information for few-shot charge prediction. Specifically, we extract coarse- and fine-grained features to enhance the model’s capability on representation, based on which the few-shot charges can be better distinguished. Furthermore, we propose a loss function based on mutual information. This loss function leverages the prior distribution of the charges to tune their weights, so the few-shot charges can contribute more on model optimization. Experimental results on several datasets demonstrate the effectiveness and robustness of our method. Besides, our method can work well on tiny datasets and has better efficiency in the training, which provides better applicability in real scenarios.
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Notes
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We suggest the reader to refer to the original paper [20] for the details of capsule network.
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Acknowledgements
We thank all the anonymous reviewers for their insightful comments. This work was supported by National Natural Science Foundation of China No. 61872370 and No. 61832017, and Beijing Outstanding Young Scientist Program NO. BJJWZYJH012019100020098.
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Zhang, H., Dou, Z., Zhu, Y., Wen, J. (2021). Few-Shot Charge Prediction with Multi-grained Features and Mutual Information. In: Li, S., et al. Chinese Computational Linguistics. CCL 2021. Lecture Notes in Computer Science(), vol 12869. Springer, Cham. https://doi.org/10.1007/978-3-030-84186-7_26
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