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Neural IR models, such as DRMM and PACRR, have achieved strong results bysuccessfully capturing relevance matching signals. We argue that the context ofthese matching signals is also important. Intuitively, when extracting,modeling, and combining matching signals, one would like to consider thesurrounding text (local context) as well as other signals from the samedocument that can contribute to the overall relevance score. In this work, wehighlight three potential shortcomings caused by not considering contextinformation and propose three neural ingredients to address them: adisambiguation component, cascade k-max pooling, and a shuffling combinationlayer. Incorporating these components into the PACRR model yields Co-PACRR, anovel context-aware neural IR model. Extensive comparisons with establishedmodels on Trec Web Track data confirm that the proposed model can achievesuperior search results. In addition, an ablation analysis is conducted to gaininsights into the impact of and interactions between different components. Werelease our code to enable future comparisons.

Co-PACRR: A Context-Aware Neural IR Model for Ad-hoc Retrieval