Cross-Level Matching Model for Information Retrieval

  • Yifan Nie
  • Jian-Yun NieEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12004)


Recently, many neural retrieval models have been proposed and shown competitive results. In particular, interaction-based models have shown superior performance to traditional models in a number of studies. However, the interactions used as the basic matching signals are between single terms or their embeddings. In reality, a term can often match a phrase or even longer segment of text. This paper proposes a Cross-Level Matching Model which enhances the basic matching signals by allowing terms to match hidden representation states within a sentence. A gating mechanism aggregates the learned matching patterns of different matching channels and outputs a global matching score. Our model provides a simple and effective way for word-phrase matching.


Information retrieval Neural network Ranking 


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.University of MontrealMontrealCanada

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