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

Understand

Neural IR models, such as DRMM and PACRR, have achieved strong results by successfully capturing relevance matching signals.

• We argue that the context of these matching signals is also important.
• Intuitively, when extracting, modeling, and combining matching signals, one would like to consider the surrounding text (local context) as well as other signals from the same document that can contribute to the overall relevance score.
• In this work, we highlight three potential shortcomings caused by not considering context information and propose three neural ingredients to address them: a disambiguation component, cascade k-max pooling, and a shuffling combination layer.