# A Sentence Prediction Approach Incorporating Trial Logic Based on Abductive Learning
## 1 Introduction  
With the construction of the rule of law, the large number of cases makes the court overburdened, and the time required to train highly qualified judges makes it impossible to fundamentally solve the contradiction of having too many cases and too few judges by simply increasing trial resources and increasing the number of judges. Therefore, we can use sentencing prediction techniques to reduce the burden on judges. Sentencing prediction is an important aspect of intelligent justice trials that aim to use computer technology to predict sentences from semi-structured adjudication documents. As shown in Figure 1, the adjudication documents are mainly composed of the court case number, name of the court, defendant information, description of the case, judgment elements, legal articles involved, and trial results. Among the present methods, there are many that use neural networks for sentencing prediction. This can be roughly divided into threedifferent solutions: first, feature extraction of the case description part in the adjudication document; second, regression prediction by connecting a layer of the neural network directly afterwards; third, dividing the sentence into intervals to transform it into a classification problem. However, these strategies only use the case description part of the decision document without using external knowledge such as the law, and these rely on the lack of interpretation of a large amount of data. [PERSON] (2018) uses inverse abductive learning, which combines machine learning and logical reasoning, and uses the relationships summarized in the data as external knowledge to solve the problem of a small amount of labeled data, called semi-supervised abductive learning (SS-ABL); yet, it still lacks the explanatory and logical aspects of the trial. Traditional methods use a neural network to directly fit the final sentence, which does not fit the logic and flow of the trial. Thus, the traditional methods of sentence prediction have the following problems: they do not make full use of the external knowledge of legal features; they do not refer to the process of the judge's trial, and the reference basis of the trial is not clear; the method of judging the sentence is not in line with the process and logic. We conducted the following research to address these issues. We noticed in this particular area of law that there is some logical and rule-based knowledge, such as legal texts and judicial interpretations. These rules can be used as a priori knowledge and act as a weak supervisor in sentencing prediction, which is not used in the traditional approach, such as: (1) If the clause about intentional homicide appears in the adjudication document, the circumstance of intentional homicide must have appeared in the case of the offender, and then, the sentence will be increased at the trial. (2) It is clearly stated in the law that different degrees of crime correspond to different levels of sentencing. For instance, when the amount of theft is smaller, the sentence in the law will be a shorter range. When the amount is larger, the sentence is in a longer range. This makes different amounts of theft under the provisions of the law affect the base sentence, and thus the final sentence. Thus, we note that there is a certain logical nature between key circumstance elements, legal provisions, and legal interpretations. This nature can be applied to logical reasoning, allowing it to play a supervisory role in both the identification of key circumstance elements and the calculation of the sentence. If the result of the identification does not conform to the logic in the law, one of the results can be changed to make it conform to that logic to the maximum extent possible. This allows for explanatory correction of misidentified labels and improves recognition rates. First of all, in order to use this external knowledge, we write the external knowledge in the form of first-order logic to form a logical knowledge base for logical reasoning.