Four Mode Based Dialogue Management with Modified POMDP Model

This thesis proposes a method to manage the interaction between the user and the system dynamically, through speech or text input which updates the user goals, select system actions and calculate rewards for each system response at each time-stamp. The main focus is made on the dialog manager, which decides how to continue the dialogue. We have used POMDP technique, as it maintains a belief distribution on the dialogue states based on the observations over the dialogue even in a noisy environment. Four contextual control modes are introduced in dialogue management for decision-making mechanism, and to keep track of machine behaviour for each dialogue state. The result obtained proves that our proposed framework has overcome the limitations of prior POMDP methods, and exactly understands the actual intention of the users within the available time, providing very interactive conversation between the user and the computer

A Personalized System for Conversational Recommendations

Searching for and making decisions about information is becoming increasingly difficult as the amount of information and number of choices increases. Recommendation systems help users find items of interest of a particular type, such as movies or restaurants, but are still somewhat awkward to use. Our solution is to take advantage of the complementary strengths of personalized recommendation systems and dialogue systems, creating personalized aides. We present a system -- the Adaptive Place Advisor -- that treats item selection as an interactive, conversational process, with the program inquiring about item attributes and the user responding. Individual, long-term user preferences are unobtrusively obtained in the course of normal recommendation dialogues and used to direct future conversations with the same user. We present a novel user model that influences both item search and the questions asked during a conversation. We demonstrate the effectiveness of our system in significantly reducing the time and number of interactions required to find a satisfactory item, as compared to a control group of users interacting with a non-adaptive version of the system

An overview of computer-based natural language processing

Computer based Natural Language Processing (NLP) is the key to enabling humans and their computer based creations to interact with machines in natural language (like English, Japanese, German, etc., in contrast to formal computer languages). The doors that such an achievement can open have made this a major research area in Artificial Intelligence and Computational Linguistics. Commercial natural language interfaces to computers have recently entered the market and future looks bright for other applications as well. This report reviews the basic approaches to such systems, the techniques utilized, applications, the state of the art of the technology, issues and research requirements, the major participants and finally, future trends and expectations. It is anticipated that this report will prove useful to engineering and research managers, potential users, and others who will be affected by this field as it unfolds

Wegho Chatbot

Esta dissertação foi escrita para acompanhar o projeto de construção de um Chatbot para o Marketplace Wegho, de a modo melhorar o seu serviço de Apoio ao Cliente. Este Chatbot foi desenvolvido utilizando o Azure Bot Service da Microsoft. A Wegho é um Marketplace que oferece tipos diferentes de serviços como limpeza doméstica e reparações, e que funciona através de um website e de aplicações móveis, disponíveis para iOS e Android. Estes serviços são disponibilizados através de trabalhadores profissionais e qualificados. Através do Marketplace um cliente pode marcar um ou mais serviços para uma data aceite entre as duas entidades, por um preço competitivo. O Chatbot desenvolvido tem como propósito principal resolver simples problemas que um cliente possa ter, tal como responder a questões frequentes. Também é capaz de gerir serviços, seja a criar um novo ou a alterar ou cancelar os que já estiverem previamente marcados. Este serviço funciona através de conversas naturais com o cliente, como se este estivesse a falar com outra pessoa, percebendo o que lhe é solicitado, qual o objetivo desejado com cada mensagem, e qual a informação enviada. Nos casos em que o Chatbot não é capaz de “compreender” o que o utilizador necessita, é capaz de entender que é o caso e então redirecioná-lo para um agente humano do serviço de Apoio ao Cliente. Este serviço foi avaliado através de um período de testes, tendo utilizadores de teste a falar com o Chatbot com objetivos específicos, e gravando as transcrições destas conversas. No fim de cada conversa o utilizador teve também a possibilidade de dar feedback para que possa, posteriormente, ser analisado. Com estas transcrições outras métricas foram retiradas para avaliação, tais como a qualidade dos pares de mensagens entre o utilizador e o Chatbot, a quantidade destes, se as respostas são suficientemente diretas ou demasiado ambíguas, entre outras.This dissertation was written to support the project of the construction of a Chatbot service for the Wegho Marketplace in order to improve the Customer Support service. This Chatbot was built using Microsoft’s Azure Bot Service. Wegho is a Marketplace that offers different kinds of services such as domestic cleaning and home repairs, and works through a website and mobile apps, on iOS and Android. These services are provided using professional and qualified workers. Through the Marketplace a customer can schedule one or more service on a suitable date, for a competitive price. The Chatbot developed has as its main purpose the solving of simple customer issues, such as answering frequently asked questions. It is also able to handle some service management, such as creating a new one, or modifying and cancelling previously existing ones. This service works through natural conversations with the customer, understanding what is required of him, what the goal intended with each message sent to it is as well as the information sent. In cases where the Chatbot doesn’t “understand” what the customer wants of it, it recognizes it and redirects him or her to a human agent within the Customer Support service. This service was evaluated through a test period, having test users talk with the Chatbot with specific goals, and saving the transcripts of these conversations. At the end of each conversation the user also gives feedback so that it may later be analysed. From these transcripts another metrics to then be evaluated were taken, such as the quality of utterance pairs between the user and the Chatbot, the quantity of these, if the responses are direct or more ambiguous, among others

Structure of a Formal User Model for Construction Information Retrieval

Information science researchers and developers have spent many years addressing the problem of retrieving the exact information needed and using it for analysis purposes. In informationseeking dialogues, the user, i.e. construction project manager or supplier, often asks questions about specific aspects of the tasks they want to perform. But most of the time it is difficult for the software systems to unambiguously understand their overall intentions. The existence of information tunnels (Tannenbaum 2002) aggravates this phenomenon. This study includes a detailed case study of the material management process in the construction industry. Based on this case study, the structure of a formal user model for information retrieval in construction management is proposed. This prototype user model will be incorporated into the system design for construction information management and retrieval. This information retrieval system is a user-centered product based on the development of a user configurable visitor mechanism for managing and retrieving project information without worrying too much about the underlying data structure of the database system. An executable UML model combined with OODB is used to reduce the ambiguity in the user's intentions and to achieve user satisfaction

TalkToModel: Explaining Machine Learning Models with Interactive Natural Language Conversations

Machine Learning (ML) models are increasingly used to make critical decisions in real-world applications, yet they have become more complex, making them harder to understand. To this end, researchers have proposed several techniques to explain model predictions. However, practitioners struggle to use these explainability techniques because they often do not know which one to choose and how to interpret the results of the explanations. In this work, we address these challenges by introducing TalkToModel: an interactive dialogue system for explaining machine learning models through conversations. Specifically, TalkToModel comprises of three key components: 1) a natural language interface for engaging in conversations, making ML model explainability highly accessible, 2) a dialogue engine that adapts to any tabular model and dataset, interprets natural language, maps it to appropriate explanations, and generates text responses, and 3) an execution component that constructs the explanations. We carried out extensive quantitative and human subject evaluations of TalkToModel. Overall, we found the conversational system understands user inputs on novel datasets and models with high accuracy, demonstrating the system's capacity to generalize to new situations. In real-world evaluations with humans, 73% of healthcare workers (e.g., doctors and nurses) agreed they would use TalkToModel over baseline point-and-click systems for explainability in a disease prediction task, and 85% of ML professionals agreed TalkToModel was easier to use for computing explanations. Our findings demonstrate that TalkToModel is more effective for model explainability than existing systems, introducing a new category of explainability tools for practitioners. Code & demo released here: https://github.com/dylan-slack/TalkToModel.Comment: Pre-print; comments welcome! Reach out to [email protected] v3 update title and abstrac