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

TL;DR

This paper presents a rule-based open-domain question-answering system capable of handling questions from any domain, tested on SQuAD v2.0, highlighting its structure and performance analysis.

Contribution

It introduces a novel rule-based approach for open-domain QA systems, addressing limitations of data-dependent statistical methods.

Findings

System answered questions satisfactorily on SQuAD v2.0

The structure of the system is detailed and analyzed

Performance results demonstrate effectiveness of the rule-based approach

Abstract

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.