Hierarchical Ranking for Answer Selection
This work addresses answer selection for question-answering systems, presenting an incremental improvement over existing methods.
The paper tackles answer selection by proposing a hierarchical ranking strategy with three levels (point, pair, list) that use different supervisory perspectives to rank candidate answers, achieving state-of-the-art non-BERT performance on TREC-QA and WikiQA datasets.
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.