# Applying Deep Learning to Answer Selection: A Study and An Open Task

arXiv: https://arxiv.org/abs/1508.01585 · HTML: https://mlsanity.com/paper/1508.01585/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2015.

## Built on by (most cited first, 24 of 24)

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- [Long-context Non-factoid Question Answering in Indic Languages](https://mlsanity.com/paper/2504.13615/) (2025)
- [INSEva: A Comprehensive Chinese Benchmark for Large Language Models in Insurance](https://mlsanity.com/paper/2509.04455/) (2025)
- [Can Taxonomy Help? Improving Semantic Question Matching using Question Taxonomy](https://mlsanity.com/paper/2101.08201/) (2021)
- [A Multi-Size Neural Network with Attention Mechanism for Answer Selection](https://mlsanity.com/paper/2105.03278/) (2021)
- [Pre-training, Fine-tuning and Re-ranking: A Three-Stage Framework for Legal Question Answering](https://mlsanity.com/paper/2412.19482/) (2024)
- [Sent2Matrix: Folding Character Sequences in Serpentine Manifolds for Two-Dimensional Sentence](https://mlsanity.com/paper/2103.08387/) (2021)
- [Deep Reinforcement Agent for Efficient Instant Search](https://mlsanity.com/paper/2203.09644/) (2022)
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## Abstract (arXiv)

We apply a general deep learning framework to address the non-factoid question answering task. Our approach does not rely on any linguistic tools and can be applied to different languages or domains. Various architectures are presented and compared. We create and release a QA corpus and setup a new QA task in the insurance domain. Experimental results demonstrate superior performance compared to the baseline methods and various technologies give further improvements. For this highly challenging task, the top-1 accuracy can reach up to 65.3% on a test set, which indicates a great potential for practical use.