  Recently, retrieval models based on dense representations are dominant in
passage retrieval tasks, due to their outstanding ability in terms of capturing
semantics of input text compared to the traditional sparse vector space models.
A common practice of dense retrieval models is to exploit a dual-encoder
architecture to represent a query and a passage independently. Though
efficient, such a structure loses interaction between the query-passage pair,
resulting in inferior accuracy. To enhance the performance of dense retrieval
models without loss of efficiency, we propose a GNN-encoder model in which
query (passage) information is fused into passage (query) representations via
graph neural networks that are constructed by queries and their top retrieved
passages. By this means, we maintain a dual-encoder structure, and retain some
interaction information between query-passage pairs in their representations,
which enables us to achieve both efficiency and efficacy in passage retrieval.
Evaluation results indicate that our method significantly outperforms the
existing models on MSMARCO, Natural Questions and TriviaQA datasets, and
achieves the new state-of-the-art on these datasets.
