Open Access

ARTICLE

Evaluating Neural Dialogue Systems Using Deep Learning and Conversation History

Inshirah Ali AlMutairi^*, Ali Mustafa Qamar

Department of Computer Science, College of Computer, Qassim University, Buraydah, Saudi Arabia

* Corresponding Author: Inshirah Ali AlMutairi. Email:

Journal on Artificial Intelligence 2022, 4(3), 155-165. https://doi.org/10.32604/jai.2022.032390

Received 16 May 2022; Accepted 25 July 2022; Issue published 01 December 2022

Abstract

Neural talk models play a leading role in the growing popular building of conversational managers. A commonplace criticism of those systems is that they seldom understand or use the conversation data efficiently. The development of profound concentration on innovations has increased the use of neural models for a discussion display. In recent years, deep learning (DL) models have achieved significant success in various tasks, and many dialogue systems are also employing DL techniques. The primary issues involved in the generation of the dialogue system are acquiring perspectives into instinctual linguistics, comprehension provision, and conversation assessment. In this paper, we mainly focus on DL-based dialogue systems. The issue to be overcome under this publication would be dialogue supervision, which will determine how the framework responds to recognizing the needs of the user. The dataset utilized in this research is extracted from movies. The models implemented in this research are the seq2seq model, transformers, and GPT while using word embedding and NLP. The results obtained after implementation depicted that all three models produced accurate results. In the modern revolutionized world, the demand for a dialogue system is more than ever. Therefore, it is essential to take the necessary steps to build effective dialogue systems.

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Keywords

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