Knowledge Enhanced Hybrid Neural Network for Text Matching

Understand

Long text brings a big challenge to semantic matching due to their complicated semantic and syntactic structures.

• To tackle the challenge, we consider using prior knowledge to help identify useful information and filter out noise to matching in long text.
• To this end, we propose a knowledge enhanced hybrid neural network (KEHNN).
• The model fuses prior knowledge into word representations by knowledge gates and establishes three matching channels with words, sequential structures of sentences given by Gated Recurrent Units (GRU), and knowledge enhanced representations.