Legal Judgment Prediction via Multi-Perspective Bi-Feedback Network
Authors: Wenmian Yang, Weijia Jia, Xiaojie Zhou, Yutao Luo
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results show our model achieves significant improvements over baselines on all prediction tasks. |
| Researcher Affiliation | Academia | Wenmian Yang1,2 , Weijia Jia2,1 , Xiaojie Zhou1 and Yutao Luo1 1Department of Computer Science and Engineering, Shanghai Jiao Tong University, China 2State Key Lab of Io T for Smart City, CIS, University of Macau, Macao, SAR China |
| Pseudocode | No | The paper describes its methods in narrative text and mathematical equations, but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions using Keras framework for building neural networks but does not provide a link or explicit statement about the availability of its own source code. |
| Open Datasets | Yes | we use two public datasets from Chinese AI and Law challenge (CAIL2018) [Xiao et al., 2018], i.e., CAIL-small (the exercise stage data) and CAIL-big (the first stage data). |
| Dataset Splits | Yes | Particularly, there exists a validation set with 12,787 cases in CAIL-small. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'Keras framework', 'THULAC', and 'Stanford Core NLP toolkit', but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | We set the word embedding size dw as 200, and the dimension of the latent state ds as 256. For CNN encoder, we set the number of filters dc as 256, and the length of sliding window h as 2,3,4,5, respectively (each kind of sliding window contains 64 filters) as [Kim, 2014]... In the training part, we set the learning rate of Adam optimizer as 0.001, and the dropout probability as 0.5. The batch size of all models is 128. We train every model for 16 epochs and evaluate the final model on the testing set. |