Hierarchical Ranking for Answer Selection
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Abstract (arXiv)
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.
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References · 13
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- 2017Bilateral Multi-Perspective Matching for Natural Language Sentencescited by 120
- 2015ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairscited by 35
- 2016Attentive Pooling Networkscited by 28
- 2016A Compare-Aggregate Model for Matching Text Sequencescited by 23
- 2017Hyperbolic Representation Learning for Fast and Efficient Neural Question Answeringcited by 23
- 2019A Compare-Aggregate Model with Latent Clustering for Answer Selectioncited by 19
- 2015LSTM-based Deep Learning Models for Non-factoid Answer Selectioncited by 17
- 2016Sentence Similarity Learning by Lexical Decomposition and Compositioncited by 16
- 2017Learning to Rank Question Answer Pairs with Holographic Dual LSTM Architecturecited by 14
- 2018Multi-Task Learning with Multi-View Attention for Answer Selection and Knowledge Base Question Answeringcited by 11
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- 2019A Gated Self-attention Memory Network for Answer Selectioncited by 11
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