Design and Development of Rule-based open-domain Question-Answering System on SQuAD v2.0 Dataset
This addresses the problem of domain-specific limitations in QA systems for users needing broad coverage, but it is incremental as it applies an existing rule-based approach to a new dataset.
The authors tackled the challenge of open-domain question answering by developing a rule-based system that can answer questions from any domain using context passages, achieving satisfactory results on 1000 questions from the SQuAD 2.0 dataset.
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.