Question Similarity Modeling with Bidirectional Long Short-Term Memory Neural Network

Modeling sentence similarity all along is a challengeable task in the field of natural language processing (NLP), since ambiguity and variability of linguistic expression. Specifically, in the field of community question answering (CQA), homologous hotspot is focusing on question retrieval. To get the most similar question compared with user's query, we proposed a question model building with Bidirectional Long Short-Term Memory (BLSTM) neural networks, which as well can be used in other fields, such as sentence similarity computation, paraphrase detection, question answering and so on. We evaluated our model in labeled Yahoo! Answers data, and results show that our method achieves significant improvement over existing methods without using external resources, such as WordNet or parsers.