Evaluating the Performance of Various Deep Reinforcement Learning Algorithms for a Conversational Chatbot

Keerthana, R.; Fathima, G.; Florence, Lilly

doi:10.1109/incet51464.2021.9456321

Cited by 8 publications

(5 citation statements)

References 12 publications

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“…The trend of using both extractive-based and generative-based AI methods for chatbots is gaining popularity. Many methods of NLP, as well as NLU [13,21,25,30], ML [24,27,30,36,37,62], and deep learning, including RNN, LSTM, GRU, encoder-decoders, Seq2Seq [5,17,21,28,31,38,[48][49][50][51][52][53][54][55]57], and reinforcement learning [56,57,60,61], have been successfully applied in research related to chatbots and conversational dialogs. Table 2 summarizes several research works that were conducted using these models, the datasets used, and their advantages and disadvantages from our perspective.…”

Section: Discussion and Research Gapmentioning

confidence: 99%

“…The model's objective is to maximize its overall rewards during training, and it accomplishes this by acting in ways that can alter the environment [6]. Reinforcement learning has been utilized in chatbots [56], wherein the reward method enables the chatbot to distinguish between correct and incorrect responses. With the use of deep reinforcement learning algorithms, this chatbot can recognize the tone of a question and respond appropriately.…”

Section: Extractive-based and Generative-based Methodsmentioning

confidence: 99%

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Evaluation of an Arabic Chatbot Based on Extractive Question-Answering Transfer Learning and Language Transformers

Alruqi,

Alzahrani

2023

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Chatbots are programs with the ability to understand and respond to natural language in a way that is both informative and engaging. This study explored the current trends of using transformers and transfer learning techniques on Arabic chatbots. The proposed methods used various transformers and semantic embedding models from AraBERT, CAMeLBERT, AraElectra-SQuAD, and AraElectra (Generator/Discriminator). Two datasets were used for the evaluation: one with 398 questions, and the other with 1395 questions and 365,568 documents sourced from Arabic Wikipedia. Extensive experimental works were conducted, evaluating both manually crafted questions and the entire set of questions by using confidence and similarity metrics. Our experimental results demonstrate that combining the power of transformer architecture with extractive chatbots can provide more accurate and contextually relevant answers to questions in Arabic. Specifically, our experimental results showed that the AraElectra-SQuAD model consistently outperformed other models. It achieved an average confidence score of 0.6422 and an average similarity score of 0.9773 on the first dataset, and an average confidence score of 0.6658 and similarity score of 0.9660 on the second dataset. The study concludes that the AraElectra-SQuAD showed remarkable performance, high confidence, and robustness, which highlights its potential for practical applications in natural language processing tasks for Arabic chatbots. The study suggests that the language transformers can be further enhanced and used for various tasks, such as specialized chatbots, virtual assistants, and information retrieval systems for Arabic-speaking users.

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Section: Discussion and Research Gapmentioning

confidence: 99%

Section: Extractive-based and Generative-based Methodsmentioning

confidence: 99%

Evaluation of an Arabic Chatbot Based on Extractive Question-Answering Transfer Learning and Language Transformers

Alruqi,

Alzahrani

2023

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“…), Tables 3 and 4 show the methods and datasets used in the surveyed papers for building conversational chatbots, respectively. In terms of the methods applied in our review, reinforcement learning was the most frequently used method [1,[12][13][14]18,28]; this is one of three basic machine learning paradigms alongside supervised learning and unsupervised learning. Reinforcement learning differs from supervised learning in that it does not need to be presented with labeled input/output pairs, nor does it need to explicitly correct suboptimal actions.…”

Section: Methods and Datasets Of Conversational Chatbotsmentioning

confidence: 99%

“…Of the surveyed papers, we found that most conversational chatbots in recent years have adopted the following four directions to find and maintain the dialogue context through a good strategy and policy, including dialogue context identification, dialogue strategy optimization, word embedding enhancement, and user engagement or connection maintenance. In this review, some researchers focused on identifying the dialogue context [22,28], and some focused on optimizing the strategy to keep this context [12,18]. There is always a theme around a conversation to make the conversation meaningful, and this is what this type of chatbot aims to do.…”

Section: Objectives Of Conversational Chatbots (Rq1)mentioning

confidence: 99%

“…The outcome of this portion is more focused on achieving the objectives of context maintenance in conversational chatbots. To improve reinforcement learning to find and maintain dialogue context, studies [1,14,18,28] have focused on system architecture enhancement or the machine learning model's re-design; the ultimate objective is to prove that the enhanced dialogue system model will generate a better and human-like response. The recently published ChatGPT [1] is a good example of leveraging reinforcement learning, which has received lots of attention from researchers.…”

Section: Outcomes and Challenges Of A Conversational Chatbotmentioning

confidence: 99%

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A Review of AI-Driven Conversational Chatbots Implementation Methodologies and Challenges (1999–2022)

2023

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A conversational chatbot or dialogue system is a computer program designed to simulate conversation with human users, especially over the Internet. These chatbots can be integrated into messaging apps, mobile apps, or websites, and are designed to engage in natural language conversations with users. There are also many applications in which chatbots are used for educational support to improve students’ performance during the learning cycle. The recent success of ChatGPT also encourages researchers to explore more possibilities in the field of chatbot applications. One of the main benefits of conversational chatbots is their ability to provide an instant and automated response, which can be leveraged in many application areas. Chatbots can handle a wide range of inquiries and tasks, such as answering frequently asked questions, booking appointments, or making recommendations. Modern conversational chatbots use artificial intelligence (AI) techniques, such as natural language processing (NLP) and artificial neural networks, to understand and respond to users’ input. In this study, we will explore the objectives of why chatbot systems were built and what key methodologies and datasets were leveraged to build a chatbot. Finally, the achievement of the objectives will be discussed, as well as the associated challenges and future chatbot development trends.

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Using AI Chatbots in Education: Recent Advances Challenges and Use Case

Aleedy¹,

Atwell²,

Meshoul³

2022

Artificial Intelligence and Sustainable Computing

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Evaluating the Performance of Various Deep Reinforcement Learning Algorithms for a Conversational Chatbot

Cited by 8 publications

References 12 publications

Evaluation of an Arabic Chatbot Based on Extractive Question-Answering Transfer Learning and Language Transformers

Evaluation of an Arabic Chatbot Based on Extractive Question-Answering Transfer Learning and Language Transformers

A Review of AI-Driven Conversational Chatbots Implementation Methodologies and Challenges (1999–2022)

Using AI Chatbots in Education: Recent Advances Challenges and Use Case

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