Neural Ad-Hoc Retrieval Meets Open Information Extraction.

This paper presents the idea of systematically integrating relation triples derived from Open Information Extraction (OpenIE) with neural rankers in order to improve the performance of the ad-hoc retrieval task. This is motivated by two reasons: (1) to capture longer-range semantic associations between keywords in documents, which would not otherwise be immediately identifiable by neural rankers; and (2) identify closely mentioned yet semantically unrelated content in the document that could lead to a document being incorrectly considered to be relevant for the query. Through our extensive experiments on three widely used TREC collections, we show that our idea consistently leads to noticeable performance improvements for neural rankers on a range of metrics.