Retrieval with frozen embeddings and k-NN delivers competitive accuracy, high data efficiency, and zero hallucinations on legal multi-label annotation across ECtHR and Eurlex datasets.
CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
abstract
In this paper, we introduce the \textbf{C}hinese \textbf{AI} and \textbf{L}aw challenge dataset (CAIL2018), the first large-scale Chinese legal dataset for judgment prediction. \dataset contains more than $2.6$ million criminal cases published by the Supreme People's Court of China, which are several times larger than other datasets in existing works on judgment prediction. Moreover, the annotations of judgment results are more detailed and rich. It consists of applicable law articles, charges, and prison terms, which are expected to be inferred according to the fact descriptions of cases. For comparison, we implement several conventional text classification baselines for judgment prediction and experimental results show that it is still a challenge for current models to predict the judgment results of legal cases, especially on prison terms. To help the researchers make improvements on legal judgment prediction, both \dataset and baselines will be released after the CAIL competition\footnote{http://cail.cipsc.org.cn/}.
fields
cs.CL 2verdicts
UNVERDICTED 2representative citing papers
A two-phase external-knowledge plus number-learning-network method for multi-label charge prediction yields 3-5% macro-F1 and 5-15% micro-F1 gains when added to existing deep models on a Chinese legal dataset.
citing papers explorer
-
Retrieval-Based Multi-Label Legal Annotation: Extensible, Data-Efficient and Hallucination-Free
Retrieval with frozen embeddings and k-NN delivers competitive accuracy, high data efficiency, and zero hallucinations on legal multi-label annotation across ECtHR and Eurlex datasets.
-
An External Knowledge Enhanced Multi-label Charge Prediction Approach with Label Number Learning
A two-phase external-knowledge plus number-learning-network method for multi-label charge prediction yields 3-5% macro-F1 and 5-15% micro-F1 gains when added to existing deep models on a Chinese legal dataset.