Abstract Meaning Representation for Human-Robot Dialogue

In this research, we begin to tackle the challenge of natural language understanding (NLU) in the context of the development of a robot dialogue system. We explore the adequacy of Abstract Meaning Representation (AMR) as a conduit for NLU. First, we consider the feasibility of using existing AMR parsers for automatically creating meaning representations for robot-directed transcribed speech data. We evaluate the quality of output of two parsers on this data against a manually annotated gold-standard data set. Second, we evaluate the semantic coverage and distinctions made in AMR overall: how well does it capture the meaning and distinctions needed in our collaborative human-robot dialogue domain? We find that AMR has gaps that align with linguistic information critical for effective human-robot collaboration in search and navigation tasks, and we present task-specific modifications to AMR to address the deficiencies

A Comparison of Quaternion Neural Network Backpropagation Algorithms

This research paper focuses on quaternion neural networks (QNNs) - a type of neural network wherein the weights, biases, and input values are all represented as quaternion numbers. Previous studies have shown that QNNs outperform real-valued neural networks in basic tasks and have potential in high-dimensional problem spaces. However, research on QNNs has been fragmented, with contributions from different mathematical and engineering domains leading to unintentional overlap in QNN literature. This work aims to unify existing research by evaluating four distinct QNN backpropagation algorithms, including the novel GHR-calculus backpropagation algorithm, and providing concise, scalable implementations of each algorithm using a modern compiled programming language. Additionally, the authors apply a robust Design of Experiments (DoE) methodology to compare the accuracy and runtime of each algorithm. The experiments demonstrate that the Clifford Multilayer Perceptron (CMLP) learning algorithm results in statistically significant improvements in network test set accuracy while maintaining comparable runtime performance to the other three algorithms in four distinct regression tasks. By unifying existing research and comparing different QNN training algorithms, this work develops a state-of-the-art baseline and provides important insights into the potential of QNNs for solving high-dimensional problems

Contributos para suportar o desenvolvimento de sistemas de diálogo

With the increase of technology present in our daily routine, a specific area rose exponentially. Spoken dialogue systems are increasingly popular and useful: they provide an easier and more versatile access to large and diverse sets of information. Nowadays there is a vast knowledge base regarding this topic, as well as different systems capable of performing numerous tasks just by simply processing voice input. There are also an increasing set of tools for their development. Despite recent advances, development of dialog systems continues to be challenging. The main objective of this thesis is to contribute and make possible and simple the development of new dialogue systems for Portuguese: by selecting, adapting and combining existing tools/frameworks. Supported by the enhancements made to the selected basis framework, two different dialog systems were developed: the first is an assistant aimed at a Smart Home environment - one of areas that benefited the most with the development of dialog systems - and the second targeting accessible tourism. The first assistant was developed aligned with the Smart Green Homes project. It implied the definition of scenarios and requirements that later helped defining the ontology and the system. Another requirement for this system was the inclusion of the back-end system developed previously as part of the Smart Green Homes project. The second was aligned with the project ACTION in the area of Tourism. It was developed for users with accessibility needs, e.g., impaired movement or vision.Com o aumento da tecnologia presente na nossa rotina diária, uma área específica subiu exponencialmente. Os sistemas de diálogo são cada vez mais populares e úteis: proporcionam um acesso mais fácil e versátil a grandes e diversificados conjuntos de informação. Hoje em dia existe uma vasta base de conhecimento sobre este tema, bem como diferentes sistemas capazes de executar inúmeras tarefas através do processamento de uma entrada de voz. Há também um conjunto crescente de ferramentas para o seu desenvolvimento. Apesar dos avanços recentes, o desenvolvimento de sistemas de diálogo continua a ser um desafio. O principal objetivo desta tese é contribuir e tornar possível e simples o desenvolvimento de novos sistemas de diálogo que suportem português: selecionando, adaptando e combinando ferramentas existentes. Baseado nas melhorias feitas nas ferramentas bases selecionadas, foram desenvolvidos dois sistemas de diálogo diferentes: o primeiro é um assistente dirigido a um ambiente Smart Home - uma das áreas que mais beneficiou com o desenvolvimento de sistemas de diálogo - e o segundo visando o turismo acessível. O primeiro assistente foi desenvolvido alinhado com o projeto Smart Green Homes. Implicou a definição de cenários e requisitos que mais tarde ajudaram a definir a ontologia e o sistema. Outro requisito para este sistema foi a inclusão do sistema back-end desenvolvido anteriormente como parte do projeto Smart Green Homes. O segundo assistente foi alinhado com o projeto ACTION na área do Turismo. Foi desenvolvido para utilizadores com necessidades de acessibilidade, por exemplo, deficiência motora ou invisualidade.Mestrado em Engenharia de Computadores e Telemátic