  Question Answering (QA) systems are used to provide proper responses to
users' questions automatically. Sentence matching is an essential task in the
QA systems and is usually reformulated as a Paraphrase Identification (PI)
problem. Given a question, the aim of the task is to find the most similar
question from a QA knowledge base. In this paper, we propose a Multi-task
Sentence Encoding Model (MSEM) for the PI problem, wherein a connected graph is
employed to depict the relation between sentences, and a multi-task learning
model is applied to address both the sentence matching and sentence intent
classification problem. In addition, we implement a general semantic retrieval
framework that combines our proposed model and the Approximate Nearest Neighbor
(ANN) technology, which enables us to find the most similar question from all
available candidates very quickly during online serving. The experiments show
the superiority of our proposed method as compared with the existing sentence
matching models.
