492 research outputs found
Simulating the Machine Translation of Low-Resource Languages by Designing a Translator Between English and an Artificially Constructed Language
Natural language processing (NLP), or the use of computers to analyze natural language, is a field that relies heavily on syntax. It would seem intuitive that computers would thrive in this area due to their strict syntax requirements, but the syntax of natural languages leaves them unable to properly parse and generate sentences that seem normal to the average speaker. A subfield of NLP, machine translation, works mainly to computerize translation between different languages. Unfortunately, such translation is not without its weaknesses; language documentation is not created equal, and many low-resource languages—languages with relatively few kinds of documentation, most often written—are left with no way to effectively benefit from machine translation. As a step toward better translation processors for low-resource languages, this thesis examined the possibility of machine translation between high resource languages and low resource languages through an analysis of different machine learning techniques, and ultimately constructing a simple translator between English and an artificially constructed language using a context-free grammar (CFG)
Usefulness, localizability, humanness, and language-benefit: additional evaluation criteria for natural language dialogue systems
Human–computer dialogue systems interact with human users using natural language. We used the ALICE/AIML chatbot architecture as a platform to develop a range of chatbots covering different languages, genres, text-types, and user-groups, to illustrate qualitative aspects of natural language dialogue system evaluation. We present some of the different evaluation techniques used in natural language dialogue systems, including black box and glass box, comparative, quantitative, and qualitative evaluation. Four aspects of NLP dialogue system evaluation are often overlooked: “usefulness” in terms of a user’s qualitative needs, “localizability” to new genres and languages, “humanness” or “naturalness” compared to human–human dialogues, and “language benefit” compared to alternative interfaces. We illustrated these aspects with respect to our work on machine-learnt chatbot dialogue systems; we believe these aspects are worthwhile in impressing potential new users and customers
A Primer on Seq2Seq Models for Generative Chatbots
The recent spread of Deep Learning-based solutions for Artificial Intelligence and the development of Large Language Models has pushed forwards significantly the Natural Language Processing area. The approach has quickly evolved in the last ten years, deeply affecting NLP, from low-level text pre-processing tasks –such as tokenisation or POS tagging– to high-level, complex NLP applications like machine translation and chatbots. This paper examines recent trends in the development of open-domain data-driven generative chatbots, focusing on the Seq2Seq architectures. Such architectures are compatible with multiple learning approaches, ranging from supervised to reinforcement and, in the last years, allowed to realise very engaging open-domain chatbots. Not only do these architectures allow to directly output the next turn in a conversation but, to some extent, they also allow to control the style or content of the response. To offer a complete view on the subject, we examine possible architecture implementations as well as training and evaluation approaches. Additionally, we provide information about the openly available corpora to train and evaluate such models and about the current and past chatbot competitions. Finally, we present some insights on possible future directions, given the current research status
Chatbot development to assist patients in health care services
Dissertação de mestrado integrado em Engenharia InformáticaDados de alta qualidade sobre tratamentos médicos e de informação técnica tornaram-se
acessĂveis, criando novas oportunidades de E-SaĂşde para a recuperação de um paciente.
A implementação da aprendizagem automática nestas soluções provou ser essencial e
eficaz na elaboração de aplicações para o utilizador para aliviar a sobrecarga do sector
de saúde. Atualmente, muitas interações com os utentes são realizadas via telefonemas
e mensagens de texto. Os agentes de conversação podem responder a estas questões,
fomentando uma rápida interação com os pacientes.
O objetivo fundamental desta dissertação é prestar apoio aos pacientes, fornecendo
uma fonte de informação fidedigna que lhes permita instruir-se e esclarecer dúvidas
sobre os procedimentos e repercussões dos seus problemas de saúde. Este propósito foi
concretizado nĂŁo apenas atravĂ©s de uma plataforma Web intuitiva e acessĂvel, composta
por perguntas frequentes, mas também integrando um agente de conversação inteligente
para responder a questões.
Para este fim, cientificamente, foi necessário conduzir a investigação, implementação
e viabilidade dos agentes de conversação no domĂnio fechado para os cuidados de
saĂşde. Constitui um importante contributo para a comunidade de desenvolvimento de
chatbots, na qual se reúnem as últimas inovações e descobertas, bem os desafios actuais
da aprendizagem automática, contribuindo para a consciencialização desta área.High-quality data on medical treatments and facility-level information has become
accessible, creating new eHealth opportunities for the recuperation of a patient. Machine
learning implementation in these solutions has been proven to be essential and effective
in building user-centred applications to relieves the burden on the healthcare sector.
Nowadays, many patient interactions are handled through healthcare services via phone
calls and text message exchange. Conversation agents can provide answers to these
queries, promoting fast patient interaction.
The underlying aim of this dissertation is to assist patients by providing a reliable
source of information to educate themselves and clarify any doubts about procedures
and implications of their health issue. This purpose was achieved not only through
an intuitive and accessible web platform, with frequently asked questions, but also by
integrating an intelligent chatting agent to answer questions.
To this end, scientifically, it was necessary to conduct the research, implementation
and feasibility of closed-domain conversation agents for healthcare. It is a valuable
input for the chatbot development community, which assembles the latest innovations
and findings, as well as the current challenges of machine learning, contributing to the
awareness of this field
Designing Service-Oriented Chatbot Systems Using a Construction Grammar-Driven Natural Language Generation System
Service oriented chatbot systems are used to inform users in a conversational manner about a particular service or
product on a website. Our research shows that current systems are time consuming to build and not very accurate or satisfying to users. We find that natural language understanding and natural language generation methods are central to creating an e�fficient and useful system. In this thesis we investigate current and past methods in this research area and place particular emphasis on Construction Grammar and its computational implementation. Our research shows that users have strong emotive reactions to how these systems behave, so we also investigate the human computer interaction component. We present three systems (KIA, John and KIA2), and carry out extensive user tests on all of them, as well as comparative tests. KIA is built using existing methods, John is built with the user in mind and KIA2 is built using the construction grammar method. We found that the construction grammar approach performs well in service oriented chatbots systems, and that users preferred it over other systems
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