mlsanityData to 7 Oct 2026

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

arXiv 2204.09659 ↗First posted 27 Mar 2022cs.CLcited by 3

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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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 8 of its 31 references that are in the map. First posted on arXiv 27 Mar 2022.

Builds on

8 of 31 references.

References · 8

  1. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,115
  2. 2018Know What You Don't Know: Unanswerable Questions for SQuADcited by 1,538
  3. 2014Embedding Entities and Relations for Learning and Inference in Knowledge Basescited by 929
  4. 2016Bidirectional Attention Flow for Machine Comprehensioncited by 278
  5. 2016Machine Comprehension Using Match-LSTM and Answer Pointercited by 47
  6. 2017Making Neural QA as Simple as Possible but not Simplercited by 33
  7. 2017Two-Stage Synthesis Networks for Transfer Learning in Machine Comprehensioncited by 11
  8. 2017S-Net: From Answer Extraction to Answer Generation for Machine Reading Comprehensioncited by 6

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Cited by year

Citing papers per year: 2022: 1; 2023: 1; 2024: 1.

3 papers.

  1. 2022NAPG: Non-Autoregressive Program Generation for Hybrid Tabular-Textual Question Answeringcited by 4
  2. 2024Raw Text is All you Need: Knowledge-intensive Multi-turn Instruction Tuning for Large Language Modelcited by 3
  3. 2023Implications of Deep Circuits in Improving Quality of Quantum Question Answeringcited by 0