Topic
Chatbot
About: Chatbot is a research topic. Over the lifetime, 2415 publications have been published within this topic receiving 24372 citations. The topic is also known as: IM bot & AI chatbot.
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TL;DR: In this article , the authors search PubMed/MEDLINE, Web of Knowledge, and Google Scholar to identify chatbot use cases deployed for public health response activities during the Covid-19 pandemic.
44 citations
01 Nov 2019
TL;DR: In this article, a literature review on chatbot researches and from observation results on a chatbot-based language learning medium developed by the author indicated that chatbots have a high potential to be used as a learning medium, both as tutor in practicing language, and as independent learning medium.
Abstract: In facing industry revolution 4.0, utilizing advanced information and computer technology in educational environment is crucial. One of the advanced computation technologies that can be used for learning, especially language learning, is chatbot. Chatbot is a computer program based on artificial intelligence that can carry out conversations through audio or text. This study intends to find out and analyze the types of artificial intelligence in the form of chatbots and the possibility of their use as language learning medium. The data in this study obtained from literature review on chatbot researches, and from observation results on chatbot-based language learning medium developed by the author. The results indicated that chatbots have a high potential to be used as a language learning medium, both as tutor in practicing language, and as independent learning medium. Moreover, research results revealed that language learners are interested in using chatbots because they can be used anytime and anywhere, and they are more confident in learning languages using chatbots than when dealing directly with human tutors.
44 citations
TL;DR: This work demonstrates that a medical chatbot can help with automatic triage and pre-assessment of patients with simple symptom analysis and a conversational approach without the use of cumbersome form-based data entry.
Abstract: Automated conversational agents built with medical applications in mind, have the potential to reduce healthcare readmissions and improve accessibility to medical knowledge. In this work, we demonstrate the development and evaluation of an automated chatbot for triage and conditions assessment, based on user inputs in natural language. The implemented bot engages patients in conversation about symptoms experienced and provides a personalized pre-synopsis based on their symptoms and profile. Our chatbot system was able to predict user conditions correctly based on two sets of patient test cases with an average precision of 0.82. Our implementation demonstrates that a medical chatbot can help with automatic triage and pre-assessment of patients with simple symptom analysis and a conversational approach without the use of cumbersome form-based data entry.
44 citations
08 Jan 2019
TL;DR: A structured literature review of chatbots showed that only few first research contributions exist and current potentials and objectives of chatbot applications were outlined, and it was shown that research gaps are present.
Abstract: Chatbots become quite hyped in recent times as they can provide an intuitive and easy-to-use natural language human-computer interface. Nevertheless, they are not yet widespread in enterprises. Corresponding application areas for collaboration at digital workplaces are lacking and prior research contributions on this topic are limited. In this research paper, we aim at surveying the state of the art as well as showing future research topics. Thus, we conducted a structured literature review and showed that only few first research contributions exist. We also outline current potentials and objectives of chatbot applications. In the discussion of the results of our structured literature review, we show that research gaps are present. To tackle the research gaps, we derive open research questions.
43 citations
Posted Content•
TL;DR: A multi-lingual extension of Persona-Chat, namely XPersona, is proposed, which includes persona conversations in six different languages other than English for evaluating multilingual personalized agents and results show that the multilingual trained models outperform the translation pipeline and that they are on par with the monolingual models.
Abstract: Personalized dialogue systems are an essential step toward better human-machine interaction. Existing personalized dialogue agents rely on properly designed conversational datasets, which are mostly monolingual (e.g., English), which greatly limits the usage of conversational agents in other languages. In this paper, we propose a multi-lingual extension of Persona-Chat, namely XPersona. Our dataset includes persona conversations in six different languages other than English for building and evaluating multilingual personalized agents. We experiment with both multilingual and cross-lingual trained baselines, and evaluate them against monolingual and translation-pipeline models using both automatic and human evaluation. Experimental results show that the multilingual trained models outperform the translation-pipeline and that they are on par with the monolingual models, with the advantage of having a single model across multiple languages. On the other hand, the state-of-the-art cross-lingual trained models achieve inferior performance to the other models, showing that cross-lingual conversation modeling is a challenging task. We hope that our dataset and baselines will accelerate research in multilingual dialogue systems.
43 citations