An Ensemble Model Based on Siamese Neural Networks for the Question Pairs Matching Task

The problem of question pairs matching aims to seek whether the underlying semantics of two questions are equivalent. For WeBank Chinese question pairs which are collected from real-world intelligent customer service questions, the goal is to identify question pairs that have the same intent. In this paper, we propose an ensemble model which based on both word and character level neural networks such as the convolutional neural network (CNN), and the long short-term memory network (LSTM) for modeling semantic similarity. And we adopt an enhanced deep semantic model (R-ESIM) which is proved to be more effective for sentence modeling. Our model takes 10-fold cross-validation into account to improve the generalization ability. In the evaluation of the CCKS 2018 shared task three, our model achieves the F1 score of 0.85085 for the opening test data which ranks the second.