# Hierarchical Ranking for Answer Selection

arXiv: https://arxiv.org/abs/2102.00677 · HTML: https://mlsanity.com/paper/2102.00677/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 30 references that are in the map.
- First posted on arXiv 1 Feb 2021.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Bilateral Multi-Perspective Matching for Natural Language Sentences](https://mlsanity.com/paper/1702.03814/) (2017)
- [ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs](https://mlsanity.com/paper/1512.05193/) (2015)
- [Attentive Pooling Networks](https://mlsanity.com/paper/1602.03609/) (2016)
- [A Compare-Aggregate Model for Matching Text Sequences](https://mlsanity.com/paper/1611.01747/) (2016)
- [Hyperbolic Representation Learning for Fast and Efficient Neural Question Answering](https://mlsanity.com/paper/1707.07847/) (2017)
- [A Compare-Aggregate Model with Latent Clustering for Answer Selection](https://mlsanity.com/paper/1905.12897/) (2019)
- [LSTM-based Deep Learning Models for Non-factoid Answer Selection](https://mlsanity.com/paper/1511.04108/) (2015)
- [Sentence Similarity Learning by Lexical Decomposition and Composition](https://mlsanity.com/paper/1602.07019/) (2016)
- [Learning to Rank Question Answer Pairs with Holographic Dual LSTM Architecture](https://mlsanity.com/paper/1707.06372/) (2017)
- [Multi-Task Learning with Multi-View Attention for Answer Selection and Knowledge Base Question Answering](https://mlsanity.com/paper/1812.02354/) (2018)
- [A Gated Self-attention Memory Network for Answer Selection](https://mlsanity.com/paper/1909.09696/) (2019)

## 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.