Context Attentive Document Ranking and Query Suggestion
arXiv:1906.02329
Abstract
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.
arXiv commentAccepted to SIGIR 2019
References in corpus (7)
- Adam: A Method for Stochastic Optimization
- Sequence to Sequence Learning with Neural Networks
- On the difficulty of training Recurrent Neural Networks
- Convolutional Neural Network Architectures for Matching Natural Language Sentences
- A Deep Relevance Matching Model for Ad-hoc Retrieval
- Learning Natural Language Inference using Bidirectional LSTM model and Inner-Attention
- Match-Tensor: a Deep Relevance Model for Search