Open Access


BERT for Conversational Question Answering Systems Using Semantic Similarity Estimation

Abdulaziz Al-Besher1, Kailash Kumar1, M. Sangeetha2,*, Tinashe Butsa3
1 College of Computing and Informatics, Saudi Electronic University, Riyadh, 11673, Kingdom of Saudi Arabia
2 Department of Information Technology, SRM Institute of Science and Technology, Kattankulathur, India
3 Department of Information Technology, Harare Institute of Technology, Belvedere, Harare
* Corresponding Author: M. Sangeetha. Email:

Computers, Materials & Continua 2022, 70(3), 4763-4780.

Received 20 June 2021; Accepted 27 July 2021; Issue published 11 October 2021


Most of the questions from users lack the context needed to thoroughly understand the problem at hand, thus making the questions impossible to answer. Semantic Similarity Estimation is based on relating user’s questions to the context from previous Conversational Search Systems (CSS) to provide answers without requesting the user's context. It imposes constraints on the time needed to produce an answer for the user. The proposed model enables the use of contextual data associated with previous Conversational Searches (CS). While receiving a question in a new conversational search, the model determines the question that refers to more past CS. The model then infers past contextual data related to the given question and predicts an answer based on the context inferred without engaging in multi-turn interactions or requesting additional data from the user for context. This model shows the ability to use the limited information in user queries for best context inferences based on Closed-Domain-based CS and Bidirectional Encoder Representations from Transformers for textual representations.


Semantic similarity estimation; conversational search; multi-turn interactions; context inference; BERT; user intent

Cite This Article

A. Al-Besher, K. Kumar, M. Sangeetha and T. Butsa, "Bert for conversational question answering systems using semantic similarity estimation," Computers, Materials & Continua, vol. 70, no.3, pp. 4763–4780, 2022.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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