# Design and Development of Rule-based open-domain Question-Answering System on SQuAD v2.0 Dataset

arXiv: https://arxiv.org/abs/2204.09659 · HTML: https://mlsanity.com/paper/2204.09659/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 31 references that are in the map.
- First posted on arXiv 27 Mar 2022.

## Builds on (most cited first, 8 of 8)

- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [Embedding Entities and Relations for Learning and Inference in Knowledge Bases](https://mlsanity.com/paper/1412.6575/) (2014)
- [Bidirectional Attention Flow for Machine Comprehension](https://mlsanity.com/paper/1611.01603/) (2016)
- [Machine Comprehension Using Match-LSTM and Answer Pointer](https://mlsanity.com/paper/1608.07905/) (2016)
- [Making Neural QA as Simple as Possible but not Simpler](https://mlsanity.com/paper/1703.04816/) (2017)
- [Two-Stage Synthesis Networks for Transfer Learning in Machine Comprehension](https://mlsanity.com/paper/1706.09789/) (2017)
- [S-Net: From Answer Extraction to Answer Generation for Machine Reading Comprehension](https://mlsanity.com/paper/1706.04815/) (2017)

## Built on by (most cited first, 3 of 3)

- [NAPG: Non-Autoregressive Program Generation for Hybrid Tabular-Textual Question Answering](https://mlsanity.com/paper/2211.03462/) (2022)
- [Raw Text is All you Need: Knowledge-intensive Multi-turn Instruction Tuning for Large Language Model](https://mlsanity.com/paper/2407.03040/) (2024)
- [Implications of Deep Circuits in Improving Quality of Quantum Question Answering](https://mlsanity.com/paper/2305.07374/) (2023)

## Abstract (arXiv)

Human mind is the palace of curious questions that seek answers. Computational resolution of this challenge is possible through Natural Language Processing techniques. Statistical techniques like machine learning and deep learning require a lot of data to train and despite that they fail to tap into the nuances of language. Such systems usually perform best on close-domain datasets. We have proposed development of a rule-based open-domain question-answering system which is capable of answering questions of any domain from a corresponding context passage. We have used 1000 questions from SQuAD 2.0 dataset for testing the developed system and it gives satisfactory results. In this paper, we have described the structure of the developed system and have analyzed the performance.