Showing papers on "Chatbot published in 2007"

Chatbots: are they really useful?

Abstract: Chatbots are computer programs that interact with users using natural lan- guages. This technology started in the 1960’s; the aim was to see if chatbot systems could fool users that they were real humans. However, chatbot sys- tems are not only built to mimic human conversation, and entertain users. In this paper, we investigate other applications where chatbots could be useful such as education, information retrival, business, and e-commerce. A range of chatbots with useful applications, including several based on the ALICE/AIML architecture, are presented in this paper.

Bringing chatbots into education: Towards natural language negotiation of open learner models

Abstract: There is an extensive body of work on Intelligent Tutoring Systems: computer environments for education, teaching and training that adapt to the needs of the individual learner. Work on personalisation and adaptivity has included research into allowing the student user to enhance the system's adaptivity by improving the accuracy of the underlying learner model. Open Learner Modelling, where the system's model of the user's knowledge is revealed to the user, has been proposed to support student reflection on their learning. Increased accuracy of the learner model can be obtained by the student and system jointly negotiating the learner model. We present the initial investigations into a system to allow people to negotiate the model of their understanding of a topic in natural language. This paper discusses the development and capabilities of both conversational agents (or chatbots) and Intelligent Tutoring Systems, in particular Open Learner Modelling. We describe a Wizard-of-Oz experiment to investigate the feasibility of using a chatbot to support negotiation, and conclude that a fusion of the two fields can lead to developing negotiation techniques for chatbots and the enhancement of the Open Learner Model. This technology, if successful, could have widespread application in schools, universities and other training scenarios.

Extracting chatbot knowledge from online discussion forums

Abstract: This paper presents a novel approach for extracting high-quality 〈thread-title, reply〉 pairs as chat knowledge from online discussion forums so as to efficiently support the construction of a chatbot for a certain domain. Given a forum, the high-quality 〈thread-title, reply〉 pairs are extracted using a cascaded framework. First, the replies logically relevant to the thread title of the root message are extracted with an SVM classifier from all the replies, based on correlations such as structure and content. Then, the extracted 〈thread-title, reply〉 pairs are ranked with a ranking SVM based on their content qualities. Finally, the Top-N 〈thread-title, reply〉 pairs are selected as chatbot knowledge. Results from experiments conducted within a movie forum show the proposed approach is effective.

Different measurement metrics to evaluate a chatbot system

Abstract: 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 user-supplied training corpus. We advocate open-ended trials by real users, such as an example Afrikaans chatbot for Afrikaans-speaking 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.

User-to-user communication method, program, and apparatus

Abstract: A user-to-user communication system allows users to chat with each other by sending messages via a computer. The computer also includes a chatbot with which the users can chat; that chatbot generates response messages automatically. The computer keeps a history of messages sent by the users, infers users' interests from the content of the messages they send, matches users with similar interests, and presents the matched users as suitable chat partners. In inferring users' interests, the computer also considers message sent to the chatbot, so that a user can be matched with suitable partners even before the user has accumulated an extensive history of communication with other users.

Analysis of user interaction with service oriented chatbot systems

Abstract: Service oriented chatbot systems are designed to help users access information from a website more easily. The system uses natural language responses to deliver the relevant information, acting like a customer service representative. In order to understand what users expect from such a system and how they interact with it we carried out two experiments which highlighted different aspects of interaction. We observed the communication between humans and the chatbots, and then between humans, applying the same methods in both cases. These findings have enabled us to focus on aspects of the system which directly affect the user, meaning that we can further develop a realistic and helpful chatbot.

A Chatbot-based Interactive Question Answering System

Abstract: Interactive question answering (QA) systems, where a dialogue interface enables followup and clarification questions, are a recent field of research. We report our experience on the design, implementation and evaluation of a chatbot-based dialogue interface for our open-domain QA system, showing that chatbots can be effective in supporting interactive QA.

Fostering Language Learner Autonomy Through Adaptive Conversation Tutors

Abstract: CALL can be a route to learner autonomy, allowing students to use PC-based software to learn individually, without need of class or teacher. However, language is above all a medium for communication, implying a dialogue of two or more participants: although CALL can provide exercises and lessons in grammar, vocabulary, writing skills etc, it might seem that conversation practice still calls for a teacher, or at least fellow students. A chatbot is a program which can chat in natural language, on a topic built into the chatbot’s internal linguistic knowledge model. Many chatbots exist, with different “knowledge” programmed by the chatbot builder. A chatbot may appear to be a suitable partner for conversation practice; for example, the speak2me.net website includes Lucy, who “chats” in the language and style of a nice, polite British young lady. However, language teachers will know that conversation practice is normally on a specific topic, to learn topic-specific vocabulary and language. Lucy is nice to chat with initially, but doesn’t adapt to different topics or lessons. Furthermore, Lucy helps autonomous learners of English, but not other languages. We have developed algorithms for adapting or retraining a chatbot with a corpus, to chat in the language and topic of the training corpus. An attraction of the corpus-training approach is that in principle any corpus, in any language and on any topic, can be used; so we have gone on to test this principle, by using domain-specific corpora to train chatbots to chat on specific topics such as the Qu’ran , Computing Frequently Asked Questions, and non-English language corpora such as the Corpus of Spoken Afrikaans. Language learners and teachers have given evaluation feedback, indicating that these adaptive chatbots offer a useful autonomous alternative to traditional classroom-based conversation practice.

CALMsystem: A Conversational Agent for Learner Modelling

Abstract: This paper describes a system which incorporates natural language technologies, database manipulation and educational theories in order to offer learners a Negotiated Learner Model, for integration into an Intelligent Tutoring System. The system presents the learner with their learner model, offering them the opportunity to compare their own beliefs regarding their capabilities with those inferred by the system. A conversational agent, or “chatbot” has been developed to allow the learner to negotiate over the representations held about them using natural language. The system aims to support the metacognitive goals of self-assessment and reflection, which are increasingly seen as key to learning and are being incorporated into UK educational policy. The paper describes the design of the system, and reports a user trial, in which the chatbot was found to support users in increasing the accuracy of their self-assessments, and in reducing the number of discrepancies between system and user beliefs in the learner model. Some lessons learned in the development have been highlighted and future research and experimentation directions are outlined.

A multimodal guide for the augmented campus

Abstract: The use of Personal Digital Assistants (PDAs) with ad-hoc built-in information retrieval and auto-localization functionalities can help people navigating an environment in a more natural manner compared to traditional audio/visual pre-recorded guides. In this work we propose and discuss a user-friendly, multi-modal guide system for pervasive context-aware service provision within augmented environments. The proposed system is adaptable to the user needs of mobility within a given environment; it is usable on different mobile devices and in particular on PDAs, which are used as advanced adaptive HEI (human-environment interaction) interfaces. An information retrieval service is provided that is easily accessible through spoken language interaction in cooperation with an auto-localization service. The interaction is enabled by speech recognition and synthesis technologies, and by a ChatBot system, endowed with common sense reasoning capabilities to properly interpret user speech and provide him with the requested information. This interaction mode turns to be more natural, and users are required to have only basic skills on the use of PDAs. The auto-localization service relies on a RFID-based framework, which resides partly in the mobile side of the entire system (PDAs), and partly in the environment side. In particular, RFID technology allows the system to provide users with context-related information. An implemented case study is showed that illustrates service provision in an augmented environment within university campus settings (termed "Augmented Campus"). Lastly, a discussion about user experiences while using trial services within the Augmented Campus is given.

Emotional and Domain Concept Enhancements to Alicebot

Abstract: Extensive research and development have been done in the area of human simulation and artificial intelligence and their related fields, such as common sense knowledge bases, chatterbots, natural language parsing, semantic analysis, synthetic actors, and cognitive sciences. This paper takes part in that extensive research by focusing on the improvement of human simulation in chatterbots, specifically in Alicebot, a prominent non-emotional pattern-matching chatbot. An emotion and personality model is added to Alicebot so that it can make decisions based on its emotions and personality. Alicebot is also augmented with the ability to determine what it likes or does not like based on its domain concept preferences. Finally, Alicebot will be able to generate its own text without using patterns. These improvements will allow Alicebot to better simulate responses like humans.

The perception of artificial intelligence as human by computer users

Abstract: This paper deals with the topic of 'humanness' in intelligent agents. Chatbot agents (e.g. Eliza, Encarta) had been criticized on their ability to communicate in human like conversation. In this study, a CIT approach was used for analyzing the human and non-human parts of Eliza's conversation. The result showed that Eliza could act like a human as if it could greet, maintain a theme, apply damage control, react appropriately to cue, offer a cue, use appropriate language style and have a personality. It was non human insofar as it used formal or unusual treatment of language, failed to respond to a specific question, failed to respond to a general question or implicit cue, evidenced time delays and phrases delivered at inappropriate times.

Providing Language Instructor with Artificial Intelligence Assistant

Abstract: This paper presents the preliminary results of developing HAL for CALL, an artificial intelligence assistant for language instructor. The assistant consists of a chatbot, an avatar (a three-dimensional visualization of the chatbot), a voice (text-to-speech engine interface) and interfaces to external sources of language knowledge. Some techniques used in adapting freely available chatbot for the need of a language learning system are presented. Integration of HAL with Second Life virtual world is proposed. We will discuss technical challenges and possible future work directions

Training and Evaluating an E-tutor

Abstract: This work generates an ontology for Operating Systems which map the students′ lexicon against ours. Their exploration of concepts generates data to train a system that focuses or broadens the interaction with the student to form a conversation. The data we are collecting will also be used to verify the knowledge base design itself by using inductive logic and statistical techniques to find patterns of misunderstanding. The tools allow students to create their own models as well as to ask questions to a Chatbot built on a teacher′s model. The students can mark their work against this teacher′s model. We start from a pedagogical approach and then evaluate use of our Chatbot to look for causal patterns in the learning material that can be used in future for automatic trouble shooting via a computer initiated dialogue that can discuss the structure of a subject with a student based on that particular student′s level of understanding.

Rule languages used in Tutorial chatbots programming

Abstract: The function performed by the bots in Intelligent Tutorial Systems has been presented in thepaper. Therefore we were able to justify the character of functions of the chatbot being designedby our team. Its purpose is to improve the functionality of an intelligent tutorial system. By meansof tools used for the construction of the chatbot we were able to apply the strategies and techniquesalready used by commonly known authors as well as individual methods adapted to the ideascreated in our group.