  The application of Deep Neural Networks for ranking in search engines may
obviate the need for the extensive feature engineering common to current
learning-to-rank methods. However, we show that combining simple relevance
matching features like BM25 with existing Deep Neural Net models often
substantially improves the accuracy of these models, indicating that they do
not capture essential local relevance matching signals. We describe a novel
deep Recurrent Neural Net-based model that we call Match-Tensor. The
architecture of the Match-Tensor model simultaneously accounts for both local
relevance matching and global topicality signals allowing for a rich interplay
between them when computing the relevance of a document to a query. On a large
held-out test set consisting of social media documents, we demonstrate not only
that Match-Tensor outperforms BM25 and other classes of DNNs but also that it
largely subsumes signals present in these models.
