Papers by Cunchao Tu
Legal Judgment Prediction via Topological Learning (D18-1)
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| Challenge: | Existing studies focus on a specific subtask of judgment prediction and ignore the dependencies among subtasks. |
| Approach: | They propose a topological multi-task learning framework that incorporates multiple subtasks and DAG dependencies into judgment prediction. |
| Outcome: | The proposed model improves on baselines on all judgment prediction tasks. |
LEVEN: A Large-Scale Chinese Legal Event Detection Dataset (2022.findings-acl)
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Feng Yao, Chaojun Xiao, Xiaozhi Wang, Zhiyuan Liu, Lei Hou, Cunchao Tu, Juanzi Li, Yun Liu, Weixing Shen, Maosong Sun
| Challenge: | Existing legal event detection datasets only cover incomprehensive event types and have limited annotated data. |
| Approach: | They present a large-scale Chinese legal event detection dataset . they use legal events as side information to promote downstream applications . |
| Outcome: | The proposed method improves 2.2 points precision in low-resource judgment prediction and 1.5 points precision for unsupervised case retrieval. |
Few-Shot Charge Prediction with Discriminative Legal Attributes (C18-1)
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| Challenge: | Existing works on charge prediction perform well on high-frequency charges but are not capable of predicting few-shot charges with limited cases. |
| Approach: | They propose an attribute-attentive charge prediction model to infer attributes and charges simultaneously . they propose to use discriminative attributes as the internal mapping between fact descriptions and charges . |
| Outcome: | The proposed model outperforms baseline models on real-world datasets by more than 50% . the proposed model can predict the attributes and charges simultaneously . |
How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence (2020.acl-main)
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| Challenge: | Legal Artificial Intelligence (LegalAI) focuses on applying artificial intelligence to help legal tasks. |
| Approach: | They introduce the history, current state, and future directions of research in LegalAI . they illustrate the tasks from the perspectives of legal professionals and NLP researchers . |
| Outcome: | The proposed system can reduce heavy and redundant work for legal professionals . it can also provide a reliable reference to those who are not familiar with the legal domain . |