Identifying High-Quality Chinese News Comments Based on Multi-Target Text Matching Model
Table 5: The correlation analysis between human scoring and our model’s scoring in different metrics. All the correlation is significant with p<0.05 (Info denotes informativeness, Cons denotes consistency, and Nove denotes novelty).
['[BOLD] Correlation', '[BOLD] Info', '[BOLD] Cons', '[BOLD] Nove', '[BOLD] Total']
To analyze the second question, we obtain our model’s scores on three metrics by removing different parts of text. We scale the output probabilities to [0, 10] so that it is comparable to the human scores. We conduct the correlation analyze between our model’s scores and the human scores. We calculate the Pearson correlation coefficient and Spearman correlation coefficient for all the three scores as well as the total score. We find that all these scores are significantly correlated (p<0.05) between human and model’s results. It concludes that our model realize the measurement of three metrics successfully. Besides, among these three scores, the correlation coefficient of informativeness is highest, which indicates that the informativeness is more important in our model.