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.

Chatbot Development as a Digital Learning Tool to Increase Students’ Research Knowledge

Abstract: The research aimed to: 1) develop the chatbot; 2) evaluate its effectiveness; and 3) investigate its effects on students’ research knowledge The sample consisted of 36 Thai university students The research instruments consisted of: 1) the chatbot; 2) an evaluation form; 3) an effectiveness questionnaire; and 4) research tests Data analysis used was mean, standard deviation, content analysis and a t-test The findings indicated that: 1) the chatbot was evaluated by experts with the applicability at a very high level ( = 467, SD = 008) with recommendation to add more research content and interactive learning The pilot test was done with 14 non-target group of students Students perceived the chatbot’s effectiveness at a high level ( = 443, SD = 035) with comments to add more examples and graphics to make the chatbot more interesting; 2) the 36 target group of Thai university students perceived the chatbot as an effective technology to use as a digital learning tool at a high level ( = 437, SD = 048) They thought that chatbot technology was easy to use, easy to understand, innovative and fun for learning They could get answers instantly and be able to seek specific information without waiting for responses However, in response to questions not matched keywords specified, further details of finding proper answers such as links should be provided instead of leaving those questions unanswered Also, the chatbot only provided responses when typing correctly so there should be an option to choose from a list of questions or keywords; and 3) the post-test scores were significantly higher than the pre-test scores at the 005 level of significance In conclusion, using chatbot technology in education settings to increase students’ research knowledge gave positive results as it led to positive learning outcomes and helped provide better personalized learning experience for students

Auto-reply email system and method with personalized content

Abstract: An auto-reply electronic mail message with personalized content. Text is extracted from an inbound email message. The text is used for chatbot input messages. Chatbot output messages are generated. The chatbot output messages are composed. The composed messages are formed into a responsive outbound email communication.

Automatically Extracting Dialog Models from Conversation Transcripts

Abstract: There is a growing need for task-oriented natural language dialog systems that can interact with a user to accomplish a given objective. Recent work on building task-oriented dialog systems have emphasized the need for acquiring task-specific knowledge from un-annotated conversational data. In our work we acquire task-specific knowledge by defining \textit{sub-task} as the key unit of a task-oriented conversation. We propose an unsupervised, apriori like algorithm that extracts the sub-tasks and their valid orderings from un-annotated human-human conversations. Modeling dialogues as a combination of sub-tasks and their valid orderings easily captures the variability in conversations. It also provides us the ability to map our dialogue model to AIML constructs and therefore use off-the-shelf AIML interpreters to build task-oriented chat-bots. We conduct experiments on real world data sets to establish the effectiveness of the sub-task extraction process. We codify the extracted sub-tasks in an AIML knowledge base and build a chatbot using this knowledge base. We also show the usefulness of the chatbot in automatically handling customer requests by performing a user evaluation study.

Find the Conversation Killers: A Predictive Study of Thread-ending Posts

Abstract: How to improve the quality of conversations in online communities has attracted considerable attention recently. Having engaged, civil, and reactive online conversations has a critical effect on the social life of Internet users. In this study, we are particularly interested in identifying a post in a multi-party conversation that is unlikely to be further replied to, which therefore kills that thread of the conversation. For this purpose, we propose a deep learning model called the ConverNet. ConverNet is attractive due to its capability of modeling the internal structure of a long conversation and its appropriate encoding of the contextual information of the conversation, through effective integration of attention mechanisms. Empirical experiments on real-world datasets demonstrate the effectiveness of the proposed model. For the widely concerned topic, our analysis also offers implications for how to improve the quality and user experience of online conversations, or how to engage users in a conversation with a chatbot.

Deep Learning Techniques for Implementation of Chatbots

Abstract: Chatbots are software programs that interact with clients using natural languages. The motto of the researchers was to know if chatbots can able to fool the clients that they were real humans. To develop a chatbot that can pass the Turing test, plenty of effort done with the introduction of the ELIZA chatbot in the year 1966. Various approaches for the development of chatbots and different technologies in the creation of chatbots developed because of those efforts. NLTK is a module in python which can able to perform Natural Language Processing. It is used to analyse the input in the form of speech and generate responses that are similar to humans. Nowadays there is a lot of demand for virtual assistants such as Siri, Cortana, Google Assistant and Alexa, and speech-based search engines. Nowadays Chatbots are gaining massive demand mainly in the business sector for automating client service and also for reducing efforts of humans. Chatbots typically used for information acquisition in dialogue systems. To perfectly imitate a human response, a chatbot should examine the query asked by a client correctly and design an appropriate response. In this study we compare and discuss the different technologies used in the chatbots and also address the design and implementation of a chatbot system.