  Deep neural networks have achieved significant improvements in information
retrieval (IR). However, most existing models are computational costly and can
not efficiently scale to long documents. This paper proposes a novel End-to-End
neural ranking framework called Reinforced Long Text Matching (RLTM) which
matches a query with long documents efficiently and effectively. The core idea
behind the framework can be analogous to the human judgment process which
firstly locates the relevance parts quickly from the whole document and then
matches these parts with the query carefully to obtain the final label.
Firstly, we select relevant sentences from the long documents by a coarse and
efficient matching model. Secondly, we generate a relevance score by a more
sophisticated matching model based on the sentence selected. The whole model is
trained jointly with reinforcement learning in a pairwise manner by maximizing
the expected score gaps between positive and negative examples. Experimental
results demonstrate that RLTM has greatly improved the efficiency and
effectiveness of the state-of-the-art models.
