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.

Usability guidelines and evaluation criteria for conversational user interfaces: a heuristic and linguistic approach

Abstract: Even though conversational user interfaces (CUI) have been studied since the 1950s, it is not yet fully understood what makes them feel natural, intuitive and usable. As a result, their design and evaluation poses major challenges. In this paper, we discuss how CUIs are different from other forms of human computer interaction, and what challenges and opportunities arise from these differences. We provide an overview of relevant linguistic principles for a natural language conversation and look at established high-level usability heuristics to derive a set of 53 technology-agnostic checkpoints specifically for text-based CUIs (a.k.a chatbots). These checkpoints have been evaluated with 15 professionals and academics from the fields of User Experience, Natural Language Processing, Conversation Analysis and linguistics to examine content validity. The resulting list of checkpoints provides both guidelines for the design and criteria for the evaluation of chatbots.

GoChat: Goal-oriented Chatbots with Hierarchical Reinforcement Learning

Abstract: A chatbot that converses like a human should be goal-oriented (i.e., be purposeful in conversation), which is beyond language generation. However, existing goal-oriented dialogue systems often heavily rely on cumbersome hand-crafted rules or costly labelled datasets, which limits the applicability. In this paper, we propose Goal-oriented Chatbots (GoChat), a framework for end-to-end training the chatbot to maximize the long-term return from offline multi-turn dialogue datasets. Our framework utilizes hierarchical reinforcement learning (HRL), where the high-level policy determines some sub-goals to guide the conversation towards the final goal, and the low-level policy fulfills the sub-goals by generating the corresponding utterance for response. In our experiments conducted on a real-world dialogue dataset for anti-fraud in financial, our approach outperforms previous methods on both the quality of response generation as well as the success rate of accomplishing the goal.

Automated Self-learning Chatbot Initially Build as a FAQs Database Information Retrieval System: Multi-level and Intelligent Universal Virtual Front-office Implementing Neural Network

Abstract: In this paper we propose a novel approach of an automatic creation of a chatbot exploiting only a questions-answers archive. The described model is not bound to a particular context or language, and it allows to initially retrieve information from a FAQs database using natural language, by allowing successively the automation of customer support services for the optimization of human resources thus implementing a self-learning chatbot system. We also propose a solution based on dynamical information system capable of exploiting the potential of the proposed model in a universal virtual front-office, including the statistics and tests necessary to validate the solution, and a comparison between neural network and AIML results.

AI Based Student Bot for Academic Information System using Machine Learning

Abstract: In General all the institutions like colleges sends their notes and information to students individually. Sometimes the student can�t access it quickly and repetition of data also increased. The realm of this work is to create a Chatbot for the college purpose. Our work reduces the human work to send every details and notes to all departments by email or some other medium. In this work, academic information's /details feed it to the database which will be available for the long time period. The academic information consists of information about placements details, exam time tables, semester notes and upcoming events. A Chatbot is a computer program or an artificial intelligence which conducts a conversation via auditory or textual methods. The chat bot stores the data by key words and when the user entered data is matched with the key it reply the assigned data for it. The Chatbot is created by using python language and Natural language processing. This project make use of the MySQL database to store the information. With the help of natural language processing the bot AI understand the message sent by the user and reply with the matched key value. In this Chatbot the user first need to login by their college roll number and Department. When the valid person asks about the particular information by text the information gets retrieved from the updated database that related to their department. Through this chat box the student can easily access whenever they want and the data need not to be update more than once.

Pchatbot: A Large-Scale Dataset for Personalized Chatbot

Abstract: atural language dialogue systems raise great attention recently. As many dialogue models are data-driven, high-quality datasets are essential to these systems. In this paper, we introduce Pchatbot, a large-scale dialogue dataset that contains two subsets collected from Weibo and Judicial forums respectively. To adapt the raw dataset to dialogue systems, we elaborately normalize the raw dataset via processes such as anonymization, deduplication, segmentation, and filtering. The scale of Pchatbot is significantly larger than existing Chinese datasets, which might benefit the data-driven models. Besides, current dialogue datasets for personalized chatbot usually contain several persona sentences or attributes. Different from existing datasets, Pchatbot provides anonymized user IDs and timestamps for both posts and responses. This enables the development of personalized dialogue models that directly learn implicit user personality from the user's dialogue history. Our preliminary experimental study benchmarks several state-of-the-art dialogue models to provide a comparison for future work. The dataset can be publicly accessed at Github: https://github.com/qhjqhj00/Pchatbot.