Cross-Level Matching Model for Information Retrieval

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.