Position-Aware Representations for Relevance Matching in Neural Information Retrieval

To deploy deep learning models for ad-hoc information retrieval, suitable representations of query-document pairs are needed. Such representations ought to capture all relevant information required to assess the relevance of a document for a given query, including uni-gram term overlap as well as positional information such as proximity and term dependencies. In this work, we investigate the use of similarity matrices that are able to encode such position-specific information. Extensive experiments on TREC Web Track data confirm that such representations can yield good results.