Identifying High-Quality Chinese News Comments Based on Multi-Target Text Matching Model
Table 7: Performance on the test set with different number of surrounding comments.
['[BOLD] Number', '[BOLD] Acc(%)', '[BOLD] P(%)', '[BOLD] R(%)', '[BOLD] F1(%)']
In order to assess the novelty of a given comment, we also use the surrounding comments about this news as input text. The proposed model can refer to the surrounding comments for analyzing the novelty of a given comment. The larger the number of surrounding comments , the more input information can be enriched, leading to a more accurate assessment of novelty. However, we find that when only one surrounding comment is used, the performance of the model turns worse compared to using no surrounding comment. The reason is that the model suffers a large variance in the case where there is only one comment, making the novelty score inaccurately evaluated. In our model, the mean-pooling operation is used to improve the local information and is helpful to the performance. The reason is that richer local features similar to n-grams are obtained by means of the mean-pooling operation. This shows that the model can get better representations through the mean-pooling operation, leading to better performance. Through the mean-pooling layer, several adjacent hidden state vectors are merged to output more abundant local features. Therefore, the proposed model is capable of capturing both the global and local textual semantics to form better representations for classification.