A Literature Survey of Recent Advances in Chatbots

Caldarini, Guendalina; Jaf, Sardar; McGarry, Kenneth

doi:10.3390/info13010041

Cited by 200 publications

(119 citation statements)

References 51 publications

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“…In a recent survey, ref. [80] reviewed advances in chatbots by using the common approach of acquiring scientific papers from search databases, based on certain search terms, and selecting a small subset from the lot for analysis, based on publications between 2007 and 2021. The databases they used are IEEE, ScienceDirect, Springer, Google Scholar, JSTOR, and arXiv.…”

Section: Related Workmentioning

confidence: 99%

State-of-the-Art in Open-Domain Conversational AI: A Survey

Adewumi

Liwicki

2022

Information

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We survey SoTA open-domain conversational AI models with the objective of presenting the prevailing challenges that still exist to spur future research. In addition, we provide statistics on the gender of conversational AI in order to guide the ethics discussion surrounding the issue. Open-domain conversational AI models are known to have several challenges, including bland, repetitive responses and performance degradation when prompted with figurative language, among others. First, we provide some background by discussing some topics of interest in conversational AI. We then discuss the method applied to the two investigations carried out that make up this study. The first investigation involves a search for recent SoTA open-domain conversational AI models, while the second involves the search for 100 conversational AI to assess their gender. Results of the survey show that progress has been made with recent SoTA conversational AI, but there are still persistent challenges that need to be solved, and the female gender is more common than the male for conversational AI. One main takeaway is that hybrid models of conversational AI offer more advantages than any single architecture. The key contributions of this survey are (1) the identification of prevailing challenges in SoTA open-domain conversational AI, (2) the rarely held discussion on open-domain conversational AI for low-resource languages, and (3) the discussion about the ethics surrounding the gender of conversational AI.

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Section: Related Workmentioning

confidence: 99%

State-of-the-Art in Open-Domain Conversational AI: A Survey

Adewumi

Liwicki

2022

Information

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“…The era of chatbots started a while ago with ELIZA [112] in 1966, then PARRY by Kenneth Mark Colby in 1972 [113], the first chatbot with personality, and the well-known intelligent digital assistants Amazon Alexa, Apple Siri, Google Assistant, or Microsoft Cortana, we use nowadays [114]. The main idea behind chatbots is the automation of services via conversational interfaces.…”

Section: Chatbotmentioning

confidence: 99%

Interactive Search on the Web: The Story So Far

et al. 2022

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Search on the web, specifically fetching of the relevant content, has been paid attention to since the advent of the web and particularly in recent years due to the tremendous growth in the volume of data and web pages. This paper categorizes the search services from the early days of the web to the present into keyword search engines, semantic search engines, question answering systems, dialogue systems and chatbots. As the first generation of search engines, keyword search engines have adopted keyword-based techniques to find the web pages containing the query keywords and ranking search results. In contrast, semantic search engines try to find meaningful and accurate results on the meaning and relations of things. Question-answering systems aim to find precise answers to natural language questions rather than returning a ranked list of relevant sources. As a subset of question answering systems, dialogue systems target to interact with human users through a dialog expressed in natural language. As a subset of dialogue systems, chatbots try to simulate human-like conversations. The paper provides an overview of the typical aspects of the studied search services, including process models, data preparation and presentation, common methodologies and categories.

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“…There are many definitions for what a chatbot (or chatterbot, in its original form) is, starting from the simplest one provided by Michael Mauldin in 1994 that defines a chatbot as a computer program that can talk with humans [1]. A more complete and specific definition is provided in [2,3], which defines a chatbot as "an intelligent conversational system that is able to process the human language and formulate a response which has to be relevant to the user input sentence". Although the original idea of a chatbot comes from the Turing test, in which a computer program talks with humans trying to convince them that their interlocutor is not artificial [4], in current applications, chatbots are simply designed to provide information and satisfy humans' requests [5,6], providing them with a more user-friendly interface in comparison, for instance, to a static content search of a website [7].…”

Section: Introductionmentioning

confidence: 99%

A Performance Comparison of Different Cloud-Based Natural Language Understanding Services for an Italian e-Learning Platform

Zubani

Sigalini²,

Serina

et al. 2022

Future Internet

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During the COVID-19 pandemic, the corporate online training sector has increased exponentially and online course providers had to implement innovative solutions to be more efficient and provide a satisfactory service. This paper considers a real case study in implementing a chatbot, which answers frequently asked questions from learners on an Italian e-learning platform that provides workplace safety courses to several business customers. Having to respond quickly to the increase in the courses activated, the company decided to develop a chatbot using a cloud-based service currently available on the market. These services are based on Natural Language Understanding (NLU) engines, which deal with identifying information such as entities and intentions from the sentences provided as input. To integrate a chatbot in an e-learning platform, we studied the performance of the intent recognition task of the major NLU platforms available on the market with an in-depth comparison, using an Italian dataset provided by the owner of the e-learning platform. We focused on intent recognition, carried out several experiments and evaluated performance in terms of F-score, error rate, response time, and robustness of all the services selected. The chatbot is currently in production, therefore we present a description of the system implemented and its results on the original users’ requests.

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A Literature Survey of Recent Advances in Chatbots

Cited by 200 publications

References 51 publications

State-of-the-Art in Open-Domain Conversational AI: A Survey

State-of-the-Art in Open-Domain Conversational AI: A Survey

Interactive Search on the Web: The Story So Far

A Performance Comparison of Different Cloud-Based Natural Language Understanding Services for an Italian e-Learning Platform

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