Multi-level Abstraction Convolutional Model with Weak Supervision for Information Retrieval

Yifan Nie, Alessandro Sordoni, Jian-Yun Nie

Abstract: Recent neural models for IR have produced good retrieval effectiveness compared with traditional models. Yet all of them assume that a single matching function should be used for all queries. In practice, user's queries may be of various nature which might require different levels of matching, from low level word matching to high level conceptual matching. To cope with this problem, we propose a multi-level abstraction convolutional model (MACM) that generates and aggregates several levels of matching scores. Weak supervision is used to address the problem of large training data. Experimental results demonstrated the effectiveness of our proposed MACM model.