  We present a context-aware neural ranking model to exploit users' on-task
search activities and enhance retrieval performance. In particular, a two-level
hierarchical recurrent neural network is introduced to learn search context
representation of individual queries, search tasks, and corresponding
dependency structure by jointly optimizing two companion retrieval tasks:
document ranking and query suggestion. To identify the variable dependency
structure between search context and users' ongoing search activities,
attention at both levels of recurrent states are introduced. Extensive
experiment comparisons against a rich set of baseline methods and an in-depth
ablation analysis confirm the value of our proposed approach for modeling
search context buried in search tasks.
