# Can Taxonomy Help? Improving Semantic Question Matching using Question Taxonomy

arXiv: https://arxiv.org/abs/2101.08201 · HTML: https://mlsanity.com/paper/2101.08201/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 39 references that are in the map.
- First posted on arXiv 20 Jan 2021.

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

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Semi-supervised Question Retrieval with Gated Convolutions](https://mlsanity.com/paper/1512.05726/) (2015)
- [Applying Deep Learning to Answer Selection: A Study and An Open Task](https://mlsanity.com/paper/1508.01585/) (2015)
- [SemEval-2016 Task 3: Community Question Answering](https://mlsanity.com/paper/1912.01972/) (2019)
- [SemEval-2015 Task 3: Answer Selection in Community Question Answering](https://mlsanity.com/paper/1911.11403/) (2019)
- [Deconvolutional Paragraph Representation Learning](https://mlsanity.com/paper/1708.04729/) (2017)
- [QANUS: An Open-source Question-Answering Platform](https://mlsanity.com/paper/1501.00311/) (2015)

## Built on by (most cited first, 1 of 1)

- [VIRT: Improving Representation-based Models for Text Matching through Virtual Interaction](https://mlsanity.com/paper/2112.04195/) (2021)

## Abstract (arXiv)

In this paper, we propose a hybrid technique for semantic question matching. It uses our proposed two-layered taxonomy for English questions by augmenting state-of-the-art deep learning models with question classes obtained from a deep learning based question classifier. Experiments performed on three open-domain datasets demonstrate the effectiveness of our proposed approach. We achieve state-of-the-art results on partial ordering question ranking (POQR) benchmark dataset. Our empirical analysis shows that coupling standard distributional features (provided by the question encoder) with knowledge from taxonomy is more effective than either deep learning (DL) or taxonomy-based knowledge alone.