# Learning to Rank Question Answer Pairs with Holographic Dual LSTM Architecture

arXiv: https://arxiv.org/abs/1707.06372 · HTML: https://mlsanity.com/paper/1707.06372/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2017.

## Built on by (most cited first, 14 of 14)

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- [Feature Engineering in Learning-to-Rank for Community Question Answering Task](https://mlsanity.com/paper/2309.07610/) (2023)
- [Learning to Rank Question Answer Pairs with Bilateral Contrastive Data Augmentation](https://mlsanity.com/paper/2106.11096/) (2021)
- [Hierarchical Ranking for Answer Selection](https://mlsanity.com/paper/2102.00677/) (2021)
- [Dense Feature Memory Augmented Transformers for COVID-19 Vaccination Search Classification](https://mlsanity.com/paper/2212.13898/) (2022)
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## Abstract (arXiv)

We describe a new deep learning architecture for learning to rank question answer pairs. Our approach extends the long short-term memory (LSTM) network with holographic composition to model the relationship between question and answer representations. As opposed to the neural tensor layer that has been adopted recently, the holographic composition provides the benefits of scalable and rich representational learning approach without incurring huge parameter costs. Overall, we present Holographic Dual LSTM (HD-LSTM), a unified architecture for both deep sentence modeling and semantic matching. Essentially, our model is trained end-to-end whereby the parameters of the LSTM are optimized in a way that best explains the correlation between question and answer representations. In addition, our proposed deep learning architecture requires no extensive feature engineering. Via extensive experiments, we show that HD-LSTM outperforms many other neural architectures on two popular benchmark QA datasets. Empirical studies confirm the effectiveness of holographic composition over the neural tensor layer.