Deep Relevance Ranking Using Enhanced Document-Query Interactions
This work addresses ranking accuracy for information retrieval tasks, but it is incremental as it builds directly on existing methods.
The paper tackles document relevance ranking by introducing new models that enhance context-sensitive encodings, building on prior work like DRMM and PACRR, and shows they outperform baselines including BM25, DRMM, and PACRR on datasets from BIOASQ and TREC ROBUST 2004.
We explore several new models for document relevance ranking, building upon the Deep Relevance Matching Model (DRMM) of Guo et al. (2016). Unlike DRMM, which uses context-insensitive encodings of terms and query-document term interactions, we inject rich context-sensitive encodings throughout our models, inspired by PACRR's (Hui et al., 2017) convolutional n-gram matching features, but extended in several ways including multiple views of query and document inputs. We test our models on datasets from the BIOASQ question answering challenge (Tsatsaronis et al., 2015) and TREC ROBUST 2004 (Voorhees, 2005), showing they outperform BM25-based baselines, DRMM, and PACRR.