  Traditional statistical retrieval models often treat each document as a
whole. In many cases, however, a document is relevant to a query only because a
small part of it contain the targeted information. In this work, we propose a
neural passage model (NPM) that uses passage-level information to improve the
performance of ad-hoc retrieval. Instead of using a single window to extract
passages, our model automatically learns to weight passages with different
granularities in the training process. We show that the passage-based document
ranking paradigm from previous studies can be directly derived from our neural
framework. Also, our experiments on a TREC collection showed that the NPM can
significantly outperform the existing passage-based retrieval models.
