Knowledge Enhanced Hybrid Neural Network for Text Matching
Table 3: Evaluation results on answer selection
['[EMPTY]', 'ACC']
Models that match text pairs by interaction representations like Arc2 and MatchPyramid are not better than models that perform matching with sentence embeddings like Arc1. This is because the training data is small and we fixed the word embedding in learning. LSTM based models in general performs better than CNN based models, because they can capture sequential information in sentences. KEHNN leverages both the sequential information and the prior knowledge from categories in matching by a CNN with multiple channels. Therefore, it outperforms all other methods, and the improvement is statistically significant (t-test with p-value ≤0.05). It is worthy to note that the gap between different methods is not big. This is because answers labeled as ”potential” only cover 10% of the data and are hard to predict.