Knowledge Enhanced Hybrid Neural Network for Text Matching
Table 4: Evaluation results on response selection
['[EMPTY]', 'R2@1', 'R10@1', 'R10@2', 'R10@5']
Our method outperforms baseline models on all metrics, and the improvement is statistically significant (t-test with p-value ≤0.01). In the data set, as the training data becomes large and we updated word embedding in learning, Arc2 and MatchPyraimd are much better than Arc1. LSTM based models perform better than CNN based models, which is consistent with the results in the QA task.