Siamese LSTM with Convolutional Similarity for Similar Question Retrieval

In this paper, we model the similar question retrieval task as a binary classification problem. We propose a novel approach of "1D-Siamese LSTM for cQA (1D-SLcQA)" to find the semantic similarity between a new question and existing question(s). In 1D-SLcQA, we use a combination of twin LSTM networks and a contrastive loss function to effectively memorize the long term dependencies i.e., capture semantic similarity even when the length of the answers/questions is very large (200 words). The similarity of the questions is modeled using a single network with (1D) (feature) convolution between feature vectors learned from twin LSTM layers. Experiments on large scale real world Yahoo Answers dataset show that 1D-SLcQA outperform the state of the art approach of Siamese cQA approach(SCQA).