# A Sentence Prediction Approach Incorporating Trial Logic Based on Abductive Learning
## 4 Experiments and Results
### Result Analysis  
We chose five baseline models for the comparison experiments:  
* TFIDF-SVM: We transformed sentence prediction into a classification problem by dividing the sentence intervals into one class. The text is first divided into words, then feature extraction is performed using TFIDF, classification is performed using SVM [25], and the median of the intervals is used as the prediction result. * CNN [26]: Using a CNN-based classification model with multiple filter window widths for text classification, we transformed sentence prediction into a classification problem by taking the final sentence as the middle value of the classification interval. * LSTM-FC [27]: A vectorized representation of the text using LSTM is followed by a linear layer for sentencing prediction. * BERT-FC [23]: Feature extraction is performed on the text using BERT, and sentence prediction is performed in a linear layer. * SS-ABL [1]: Sentence prediction directly using the inverse abductive learning framework. Comparative experiments are shown in Table 2.  
We can see that comparing the three models with different training set proportions, our model is better in terms of key element recognition and sentence calculation. Moreover, when the training set is 90%, the error of our model in the prediction of the prison sentence is within three months, reaching 2.6 months. Although the MAE of the SS-ABL model is only slightly lower than that of our model when the training set accounts for 10%, the MSE and RMSE are higher and show that the prediction result fluctuates greatly and does not have good stability. Again, we can see from the R2 Score that the SPITL model is better. For the R2 Score, the closer to 1, the better the fit of the model is and the better the model is. It can also be seen from the table that the R2 Score of the SPITL model is always closer to 1 than those of the other models for the same training ratio. Especially when the training accounts for a relatively small amount of time, the difference with other models is more obvious, which should be played by the use of external knowledge through logical reasoning. From the above experiments, it is clear that SPITL outperforms the traditional model. Since most traditional models are based on using data to adjust parameters to fit the results, the accuracy is not particularly high when the amount of data is small. However, this model combines machine learning and logical reasoning and uses logical reasoning to constrain and change the results of recognition when the amount of data is small, resulting in a higher recognition rate and a smaller error in the sentence prediction. For example, when it is known that the case involves Article 17 of the criminal code (About minors), then the key circumstance element must contain the \"Minor\", and if the identified pseudo-label does not contain that label, it is modified to contain that label. In addition, we verified the validity of the sentence calculation method. As shown in Table 3, when key plot elements were obtained, we used different calculation methods for comparison. This means that we used different methods for sentence calculation when the correct key elements are known. We can see that comparing some of the common methods of calculating sentences, our calculation method is superior to the other methods. When having more training sets,