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Hybrid Machine Learning Approach for Task-Oriented Dialog Systems

by Ganesh Reddy Gunnam, Devasena Inupakutika, Rahul Mundlamuri, Sahak Kaghyan, David Akopian
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 23
Year of Publication: 2024
Authors: Ganesh Reddy Gunnam, Devasena Inupakutika, Rahul Mundlamuri, Sahak Kaghyan, David Akopian
10.5120/ijca2024923679

Ganesh Reddy Gunnam, Devasena Inupakutika, Rahul Mundlamuri, Sahak Kaghyan, David Akopian . Hybrid Machine Learning Approach for Task-Oriented Dialog Systems. International Journal of Computer Applications. 186, 23 ( May 2024), 35-42. DOI=10.5120/ijca2024923679

@article{ 10.5120/ijca2024923679,
author = { Ganesh Reddy Gunnam, Devasena Inupakutika, Rahul Mundlamuri, Sahak Kaghyan, David Akopian },
title = { Hybrid Machine Learning Approach for Task-Oriented Dialog Systems },
journal = { International Journal of Computer Applications },
issue_date = { May 2024 },
volume = { 186 },
number = { 23 },
month = { May },
year = { 2024 },
issn = { 0975-8887 },
pages = { 35-42 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number23/hybrid-machine-learning-approach-for-task-oriented-dialog-systems/ },
doi = { 10.5120/ijca2024923679 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-05-31T22:32:03.063825+05:30
%A Ganesh Reddy Gunnam
%A Devasena Inupakutika
%A Rahul Mundlamuri
%A Sahak Kaghyan
%A David Akopian
%T Hybrid Machine Learning Approach for Task-Oriented Dialog Systems
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 23
%P 35-42
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Nowadays, automated chatbots are commonly used since they easily provide essential information. While generic chatbots are essential for open-domain dialog, specific applications are better served with task-oriented dialog systems. These task-oriented dialog systems typically solve particular tasks in the application where the chatbot and user know what they are discussing (both sides know the scope and context of the conversation topic). The majority of these chatbots work based on keywords. Keyword extraction has been a well-established field in the natural language processing (NLP) domain for quite some time. It is crucial in various applications, such as information retrieval, search engine optimization, and content summarization. Recently, there has been a growing interest in the contextual recognition of keywords, which aims to identify keywords in a given text based on their contextual relevance. Additionally, integrating Large Language Models (LLMs) with intent prediction (IP) has opened new possibilities for interpreting and utilizing keywords in a more context-aware manner. In particular, one such LLM, BERT, a SQuAD dataset-based NLP model, has become a popular question-answer set. However, task-oriented systems still challenge specific questions, such as yes/no and synonym-based inquiries. Thus, a hybrid model involving LLMs and IP merits additional study. This paper explores the intersection of keyword extraction, LLMs, and Intent Prediction in the context of protocol-driven chatbots, particularly those designed for task-oriented applications, emphasizing their potential in addressing a niche application. Specifically, this paper presents a hybrid approach (TaskBERT) that addresses these challenges. The evaluation results demonstrate that TaskBERT outperforms Google Dialogflow and the performant keyword extraction tool KeyBERT.

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Index Terms

Computer Science
Information Sciences

Keywords

artificial intelligence natural language processing closed domain chatbot intent prediction

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