A chatbot is a machine conversation system which interacts with human users via natural conversational language. Software to machine-learn conversational patterns from a transcribed dialogue corpus has been used to generate a range of chatbots speaking various languages and sublanguages including varieties of English, as well as French, Arabic and Afrikaans. This paper presents a program to learn from spoken transcripts of the Dialogue Diversity Corpus of English, the Minnesota French Corpus, the Corpus of Spoken Afrikaans, the Qur'an Arabic-English parallel corpus, and the British National Corpus of English; we discuss the problems which arose during learning and testing. Two main goals were achieved from the automation process. One was the ability to generate different versions of the chatbot in different languages, bringing chatbot technology to languages with few if any NLP resources: the corpus-based learning techniques transferred straightforwardly to develop chatbots for Afrikaans and Qur'anic Arabic. The second achievement was the ability to learn a very large number of categories within a short time, saving effort and errors in doing such work manually: we generated more than one million AIML categories or conversation-rules from the BNC corpus, 20 times the size of existing AIML rule-sets, and probably the biggest AI Knowledge-Base ever.
A chatbot is a software system, which can interact or "chat" with a human user in natural language such as English. For the annual Loebner Prize contest, rival chatbots have been assessed in terms of ability to fool a judge in a restricted chat session. We are investigating methods to train and adapt a chatbot to a specific user's language use or application, via a usersupplied training corpus. We advocate open-ended trials by real users, such as an example Afrikaans chatbot for Afrikaansspeaking researchers and students in South Africa. This is evaluated in terms of "glass box" dialogue efficiency metrics, and "black box" dialogue quality metrics and user satisfaction feedback. The other examples presented in this paper are the Qur'an and the FAQchat prototypes. Our general conclusion is that evaluation should be adapted to the application and to user needs.
Abstract-Web accessibility concerns of building websites that are accessible by all people regardless of their ability or disability. The W3C Web Accessibility Initiative (WAI) has been established to raise awareness of universal access. WAI develops guidelines which can help to ensure that Web pages are widely accessible. Assistive technology is used to increase, improve, and maintain capabilities of disabled persons to execute tasks that are sometimes difficult or impossible to do without technical aid. Also it helps them achieve their scholar, professional and social activities. This paper exposes an approach to investigate accessible contents of educational websites to ensure and measure its compliance with accessibility standards for visually impaired people. This study focuses on studying existing standards and investigating its applicability on educational institute websites. This will increase accessibility on e-learning materials that are provided by educational institutes. In this paper a sample of websites at selected universities in Jordan are evaluated in terms of accessibility in comparison to some universities websites in England and Arabic region. Results show that accessibility errors of universities websites in Jordan, and Arab region exceed the ones in UK by 13 times, and 5 times consequently.
Abstract-In this paper, we describe a way to access Arabic Web Question Answering (QA) corpus using a chatbot, without the need for sophisticated natural language processing or logical inference. Any Natural Language (NL) interface to Question Answer (QA) system is constrained to reply with the given answers, so there is no need for NL generation to recreate well-formed answers, or for deep analysis or logical inference to map user input questions onto this logical ontology; simple (but large) set of pattern-template matching rules will suffice. In previous research, this approach works properly with English and other European languages. In this paper, we try to see how the same chatbot will react in terms of Arabic Web QA corpus. Initial results shows that 93% of answers were correct, but because of a lot of characteristics related to Arabic language, changing Arabic questions into other forms may lead to no answers.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.