Abstract
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