Service chatbots: a systematic review
Chatbots or Conversational agents are the next significant technological leap in the field of conversational services, that is, enabling a device to communicate with a user upon receiving user requests in natural language. The device uses artificial intelligence and machine learning to respond to th...
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| Format: | Article |
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Elsevier Ltd
2021
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| _version_ | 1796866075702329344 |
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| author | Mohamad Suhaili, Sinarwati Salim, Naomie Jambli, Mohamad Nazim |
| author_facet | Mohamad Suhaili, Sinarwati Salim, Naomie Jambli, Mohamad Nazim |
| author_sort | Mohamad Suhaili, Sinarwati |
| collection | ePrints |
| description | Chatbots or Conversational agents are the next significant technological leap in the field of conversational services, that is, enabling a device to communicate with a user upon receiving user requests in natural language. The device uses artificial intelligence and machine learning to respond to the user with automated responses. While this is a relatively new area of study, the application of this concept has increased substantially over the last few years. The technology is no longer limited to merely emulating human conversation but is also being increasingly used to answer questions, either in academic environments or in commercial uses, such as situations requiring assistants to seek reasons for customer dissatisfaction or recommending products and services. The primary purpose of this literature review is to identify and study the existing literature on cutting-edge technology in developing chatbots in terms of research trends, their components and techniques, datasets and domains used, as well as evaluation metrics most used between 2011 and 2020. Using the standard SLR guidelines designed by Kitchenham, this work adopts a systematic literature review approach and utilizes five prestigious scientific databases for identifying, extracting, and analyzing all relevant publications during the search. The related publications were filtered based on inclusion/exclusion criteria and quality assessment to obtain the final review paper. The results of the review indicate that the exploitation of deep learning and reinforcement learning architecture is the most used technique to understand users’ requests and to generate appropriate responses. Besides, we also found that the Twitter dataset (open domain) is the most popular dataset used for evaluation, followed by Airline Travel Information Systems (ATIS) (close domain) and Ubuntu Dialog Corpora (technical support) datasets. The SLR review also indicates that the open domain provided by the Twitter dataset, airline and technical support are the most common domains for chatbots. Moreover, the metrics utilized most often for evaluating chatbot performance (in descending order of popularity) were found to be accuracy, F1-Score, BLEU (Bilingual Evaluation Understudy), recall, human-evaluation, and precision. |
| first_indexed | 2024-03-05T21:06:47Z |
| format | Article |
| id | utm.eprints-95718 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T21:06:47Z |
| publishDate | 2021 |
| publisher | Elsevier Ltd |
| record_format | dspace |
| spelling | utm.eprints-957182022-05-31T13:18:09Z http://eprints.utm.my/95718/ Service chatbots: a systematic review Mohamad Suhaili, Sinarwati Salim, Naomie Jambli, Mohamad Nazim QA75 Electronic computers. Computer science T58.5-58.64 Information technology Chatbots or Conversational agents are the next significant technological leap in the field of conversational services, that is, enabling a device to communicate with a user upon receiving user requests in natural language. The device uses artificial intelligence and machine learning to respond to the user with automated responses. While this is a relatively new area of study, the application of this concept has increased substantially over the last few years. The technology is no longer limited to merely emulating human conversation but is also being increasingly used to answer questions, either in academic environments or in commercial uses, such as situations requiring assistants to seek reasons for customer dissatisfaction or recommending products and services. The primary purpose of this literature review is to identify and study the existing literature on cutting-edge technology in developing chatbots in terms of research trends, their components and techniques, datasets and domains used, as well as evaluation metrics most used between 2011 and 2020. Using the standard SLR guidelines designed by Kitchenham, this work adopts a systematic literature review approach and utilizes five prestigious scientific databases for identifying, extracting, and analyzing all relevant publications during the search. The related publications were filtered based on inclusion/exclusion criteria and quality assessment to obtain the final review paper. The results of the review indicate that the exploitation of deep learning and reinforcement learning architecture is the most used technique to understand users’ requests and to generate appropriate responses. Besides, we also found that the Twitter dataset (open domain) is the most popular dataset used for evaluation, followed by Airline Travel Information Systems (ATIS) (close domain) and Ubuntu Dialog Corpora (technical support) datasets. The SLR review also indicates that the open domain provided by the Twitter dataset, airline and technical support are the most common domains for chatbots. Moreover, the metrics utilized most often for evaluating chatbot performance (in descending order of popularity) were found to be accuracy, F1-Score, BLEU (Bilingual Evaluation Understudy), recall, human-evaluation, and precision. Elsevier Ltd 2021-12-01 Article PeerReviewed Mohamad Suhaili, Sinarwati and Salim, Naomie and Jambli, Mohamad Nazim (2021) Service chatbots: a systematic review. Expert Systems with Applications, 184 . ISSN 0957-4174 http://dx.doi.org/10.1016/j.eswa.2021.115461 DOI:10.1016/j.eswa.2021.115461 |
| spellingShingle | QA75 Electronic computers. Computer science T58.5-58.64 Information technology Mohamad Suhaili, Sinarwati Salim, Naomie Jambli, Mohamad Nazim Service chatbots: a systematic review |
| title | Service chatbots: a systematic review |
| title_full | Service chatbots: a systematic review |
| title_fullStr | Service chatbots: a systematic review |
| title_full_unstemmed | Service chatbots: a systematic review |
| title_short | Service chatbots: a systematic review |
| title_sort | service chatbots a systematic review |
| topic | QA75 Electronic computers. Computer science T58.5-58.64 Information technology |
| work_keys_str_mv | AT mohamadsuhailisinarwati servicechatbotsasystematicreview AT salimnaomie servicechatbotsasystematicreview AT jamblimohamadnazim servicechatbotsasystematicreview |