Identifying High-Quality Chinese News Comments Based on Multi-Target Text Matching Model
Table 4: Comparison between our proposed model and the baselines on the test set (Acc, P, R, and F1 denote accuracy, precision, recall and F1 score, respectively).
['[BOLD] Models', '[BOLD] Acc(%)', '[BOLD] P(%)', '[BOLD] R(%)', '[BOLD] F1(%)']
Here we compare our proposed model with the baselines. Among all the traditional machine learning models, the logistic regression method achieves the highest accuracy and F1 score. However, our MTM model achieves improvements of 7.16% accuracy and 6.32% F1 score over the logistic regression model. In addition, our proposed MTM model is also able to outperform other existing neural network models by a large margin. For instance, our MTM model achieves improvements of 3.27% accuracy and 3.33% F1 score over the popular BIMPM model, which shows that the multi-target matching mechanism can effectively improve the performance of model classification. MTM model can match the comment and multi-kind of auxiliary information in a more effective way. Therefore, our proposed model is capable to learn better representation for classification.