Learning deep relevance couplings for ad-hoc document retrieval

Modeling the relevance matching between a query and a document is challenging in ad-hoc document retrieval due to diverse semantic matching aspects. Both explicit and implicit semantic matching aspects exist, corresponding to the query term occurrences in a document and the term transformations between queries and documents respectively. While most of existing neural retrieval models involve both explicit and implicit matching aspects, they do not clearly and effectively distinguish and couple explicit and implicit matching signals, resulting in either only capturing the local knowledge of query topics or lacking of in-depth understanding of user query need. In this work, we propose a deep relevance coupling model (DRC) for document retrieval to capture the diverse semantic matching aspects. DRC effectively learns and couples three aspects: explicit semantic learning, implicit semantic learning, and relevance coupling. The explicit semantic learning employs a frequency layer and a probability layer to obtain the exact term-level relevance signals between queries and documents. The implicit semantic learning adopts a transformation layer and a dependency layer to automatically capture the implicit term-level relevance signals between queries and documents. The relevance coupling transforms the term-level signals to the query-document matching signals by using a term weight layer and then integrates all the query-document matching signals into the final relevance scores between queries and documents. Experiments conducted on the TREC collections demonstrate that the DRC model delivers better retrieval accuracy and robustness than the baseline methods.