We thank the anonymous reviewers for their valuable comments. This work was supported by the Fundamental Research Funds for Central Universities of CCNU (No. CCNU15A05062).
机构署名：
本校为第一且通讯机构
院系归属：
计算机学院
摘要：
One category of neural information retrieval models tries to learn text representation in a common embedding space for both queries and documents. However, a single embedding space is not always sufficient, since queries and documents are different in terms of length, number of topics covered, etc. We argue that queries and documents should be mapped into different but overlapping embedding spaces, which is named Partially Shared Embedding Space (PSES) model in this paper. PSES consists of two embedding spaces respectively for queries and docum...
