# Accelerating Real-Time Question Answering via Question Generation

arXiv: https://arxiv.org/abs/2009.05167 · HTML: https://mlsanity.com/paper/2009.05167/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2020.

## Built on by (most cited first, 11 of 11)

- [PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them](https://mlsanity.com/paper/2102.07033/) (2021)
- [Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation](https://mlsanity.com/paper/2104.08678/) (2021)
- [A Survey on Data Augmentation in Large Model Era](https://mlsanity.com/paper/2401.15422/) (2024)
- [Retrieval-guided Counterfactual Generation for QA](https://mlsanity.com/paper/2110.07596/) (2021)
- [Peek Across: Improving Multi-Document Modeling via Cross-Document Question-Answering](https://mlsanity.com/paper/2305.15387/) (2023)
- [CATfOOD: Counterfactual Augmented Training for Improving Out-of-Domain Performance and Calibration](https://mlsanity.com/paper/2309.07822/) (2023)
- [CONSISTENT: Open-Ended Question Generation From News Articles](https://mlsanity.com/paper/2210.11536/) (2022)
- [EfficientQA : a RoBERTa Based Phrase-Indexed Question-Answering System](https://mlsanity.com/paper/2101.02157/) (2021)
- [Unified Question Generation with Continual Lifelong Learning](https://mlsanity.com/paper/2201.09696/) (2022)
- [QASnowball: An Iterative Bootstrapping Framework for High-Quality Question-Answering Data Generation](https://mlsanity.com/paper/2309.10326/) (2023)
- [QOG:Question and Options Generation based on Language Model](https://mlsanity.com/paper/2406.12381/) (2024)

## Abstract (arXiv)

Although deep neural networks have achieved tremendous success for question answering (QA), they are still suffering from heavy computational and energy cost for real product deployment. Further, existing QA systems are bottlenecked by the encoding time of real-time questions with neural networks, thus suffering from detectable latency in deployment for large-volume traffic. To reduce the computational cost and accelerate real-time question answering (RTQA) for practical usage, we propose to remove all the neural networks from online QA systems, and present Ocean-Q (an Ocean of Questions), which introduces a new question generation (QG) model to generate a large pool of QA pairs offline, then in real time matches an input question with the candidate QA pool to predict the answer without question encoding. Ocean-Q can be readily deployed in existing distributed database systems or search engine for large-scale query usage, and much greener with no additional cost for maintaining large neural networks. Experiments on SQuAD(-open) and HotpotQA benchmarks demonstrate that Ocean-Q is able to accelerate the fastest state-of-the-art RTQA system by 4X times, with only a 3+% accuracy drop.