  Answer selection is a task to choose the positive answers from a pool of
candidate answers for a given question. In this paper, we propose a novel
strategy for answer selection, called hierarchical ranking. We introduce three
levels of ranking: point-level ranking, pair-level ranking, and list-level
ranking. They formulate their optimization objectives by employing supervisory
information from different perspectives to achieve the same goal of ranking
candidate answers. Therefore, the three levels of ranking are related and they
can promote each other. We take the well-performed compare-aggregate model as
the backbone and explore three schemes to implement the idea of applying the
hierarchical rankings jointly: the scheme under the Multi-Task Learning (MTL)
strategy, the Ranking Integration (RI) scheme, and the Progressive Ranking
Integration (PRI) scheme. Experimental results on two public datasets, WikiQA
and TREC-QA, demonstrate that the proposed hierarchical ranking is effective.
Our method achieves state-of-the-art (non-BERT) performance on both TREC-QA and
WikiQA.
