Knowledge Enhanced Hybrid Neural Network for Text Matching
(a) QA dataset
['Length', '[0,30)', '[30,60)', '[60,90)', '[90,∞)']
We compared our model with 2 typical matching models: LSTM and MV-LSTM. We binned the text pairs into 4 buckets, according to the length of the concatenation of the two pieces of text. #Pair represents the number of pairs that fall into the bucket. From the results, we can see that on relatively short text (i.e., length in [0,30) ), KEHNN performs comparably well with MV-LSTM, while on long text, KEHNN significantly improves the matching accuracy. The results verified our claim that matching with multiple channels and prior knowledge can enhance accuracy on long text. Note that on the Ubuntu data, all models perform worse on short text than them on long text. This is because we ignored context for short message-response pairs, while long pairs are usually independent with context and have complete semantics.