A Review of Current Trends in the Development of Chatbot Systems

Nagarhalli, Tatwadarshi P.; Vaze, Vinod; Rana, Neeta

doi:10.1109/icaccs48705.2020.9074420

Cited by 72 publications

(31 citation statements)

References 12 publications

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“…Nagarhalli et al [102] present the current trends in the development of dialog system systems. Conversational artificial intelligent bots are one of the most important application of natural language processing.…”

Section: Deep Learning and Machine Learning Based Systemsmentioning

confidence: 99%

Do You Ever Get Off Track in a Conversation? The Conversational System’s Anatomy and Evaluation Metrics

Yadav

Kaushik

2022

Knowledge

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Conversational systems are now applicable to almost every business domain. Evaluation is an important step in the creation of dialog systems so that they may be readily tested and prototyped. There is no universally agreed upon metric for evaluating all dialog systems. Human evaluation, which is not computerized, is now the most effective and complete evaluation approach. Data gathering and analysis are evaluation activities that need human intervention. In this work, we address the many types of dialog systems and the assessment methods that may be used with them. The benefits and drawbacks of each sort of evaluation approach are also explored, which could better help us understand the expectations associated with developing an automated evaluation system. The objective of this study is to investigate conversational agents, their design approaches and evaluation metrics. This approach can help us to better understand the overall process of dialog system development, and future possibilities to enhance user experience. Because human assessment is costly and time consuming, we emphasize the need of having a generally recognized and automated evaluation model for conversational systems, which may significantly minimize the amount of time required for analysis.

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Section: Deep Learning and Machine Learning Based Systemsmentioning

confidence: 99%

Do You Ever Get Off Track in a Conversation? The Conversational System’s Anatomy and Evaluation Metrics

Yadav

Kaushik

2022

Knowledge

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“…With the rapid development of technology and advancement of machine learning algorithms, the HCI applications such as ELIZA and ALICE are gaining popularity in academia, as well as in organizations. Chatbot systems help to reduce operating costs by reducing and automating the workload to the customer center and higher management levels, resulting in increased availability for customer services [9].…”

Section: Literature Reviewmentioning

confidence: 99%

Adversarial Training for Multi Domain Dialog System

Uprety¹,

Jeong²

2022

Intelligent Automation &Amp; Soft Computing

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Natural Language Understanding and Speech Understanding systems are now a global trend, and with the advancement of artificial intelligence and machine learning techniques, have drawn attention from both the academic and business communities. Domain prediction, intent detection and entity extraction or slot fillings are the most important parts for such intelligent systems. Various traditional machine learning algorithms such as Bayesian algorithm, Support Vector Machine, and Artificial Neural Network, along with recent Deep Neural Network techniques, are used to predict domain, intent, and entity. Most language understanding systems process user input in a sequential order: domain is first predicted, then intent and slots are filled according to the semantic frames of the predicted domain. This pipeline approach, however, has many disadvantages including downstream error; i.e., if the system fails to predict the domain, then the system also fails to predict intent and slot. The main purpose of this paper is to mitigate the risk of downstream error propagation in traditional pipelined models and improve the predictive performance of domain, intent, and slot-all of which are critical steps for speech understanding and dialog systems-with a deep learning-based single joint model trained with an adversarial approach and long shortterm memory (LSTM) algorithm. The systematic experimental analysis shows significant improvements in predictive performance for domain, intent, and entity with the proposed adversarial joint model, compared to the base joint model.

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“…Concerning the topic of the paper of the improvement of customer assistance, in (Nagarhalli, et al, 2020) are shown how conversational systems can be used to develop assistance services in various domains. In addition, artificial intelligence is potentially useful in supporting consumers about shopping (Pizzi, et al, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%