The current charge prediction datasets mostly focus on single-defendant criminal cases.However, real-world criminal cases usually involve multiple defendants whose criminal facts are intertwined. In an early attempt to fill this gap, we introduce a new benchmark that encompasses legal cases involving multiple defendants, where each defendant is labeled with a charge and four types of crime elements, i.e., Object Element, Objective Element, Subject Element, and Subjective Element. Based on the dataset, we further develop an interpretable model called EJudge that incorporates crime elements and legal rules to infer charges. We observe that predicting crime charges while providing corresponding rationales benefits the interpretable AI system. Extensive experiments show that EJudge significantly surpasses state-of-the-art methods, which verify the importance of crime elements and legal rules in multi-defendant charge prediction. The source code and dataset are available at https://anonymous.4open.science/r/MCP_1-6010.

Chinese spelling correction (CSC) is a challenging task with the goal of correcting each wrong character in Chinese texts. Incorrect characters in a Chinese text are mainly due to the similar shape and similar pronunciation of Chinese characters. Recently, the paradigm of pre-training and fine-tuning has achieved remarkable success in natural language processing. However, the pre-training objectives in existing methods are not tailored for the CSC task since they neglect the visual and phonetic properties of characters, resulting in suboptimal spelling correction. In this work, we propose to pre-train a new corrector named PTCSpell for the CSC task under the detector-corrector architecture. The corrector we propose has the following two improvements. First, we design two novel pre-training objectives to capture pronunciation and shape information in Chinese characters. Second, we propose a new strategy to tackle the issue that the detector’s prediction results mislead the corrector by balancing the loss of wrong characters and correct characters. Experiments on three benchmarks (i.e., SIGHAN 2013, 2014, and 2015) show that our model achieves an average of 5.8% F1 improvements at the correction level over state-of-the-art methods, verifying its effectiveness.