Deep Dialog Act Recognition using Multiple Token, Segment, and Context Information Representations

Dialog act (DA) recognition is a task that has been widely explored over the years. Recently, most approaches to the task explored different DNN architectures to combine the representations of the words in a segment and generate a segment representation that provides cues for intention. In this study, we explore means to generate more informative segment representations, not only by exploring different network architectures, but also by considering different token representations, not only at the word level, but also at the character and functional levels. At the word level, in addition to the commonly used uncontextualized embeddings, we explore the use of contextualized representations, which provide information concerning word sense and segment structure. Character-level tokenization is important to capture intention-related morphological aspects that cannot be captured at the word level. Finally, the functional level provides an abstraction from words, which shifts the focus to the structure of the segment. We also explore approaches to enrich the segment representation with context information from the history of the dialog, both in terms of the classifications of the surrounding segments and the turn-taking history. This kind of information has already been proved important for the disambiguation of DAs in previous studies. Nevertheless, we are able to capture additional information by considering a summary of the dialog history and a wider turn-taking context. By combining the best approaches at each step, we achieve results that surpass the previous state-of-the-art on generic DA recognition on both SwDA and MRDA, two of the most widely explored corpora for the task. Furthermore, by considering both past and future context, simulating annotation scenario, our approach achieves a performance similar to that of a human annotator on SwDA and surpasses it on MRDA.Comment: 38 pages, 7 figures, 9 tables, submitted to JAI

Assessing User Expertise in Spoken Dialog System Interactions

Identifying the level of expertise of its users is important for a system since it can lead to a better interaction through adaptation techniques. Furthermore, this information can be used in offline processes of root cause analysis. However, not much effort has been put into automatically identifying the level of expertise of an user, especially in dialog-based interactions. In this paper we present an approach based on a specific set of task related features. Based on the distribution of the features among the two classes - Novice and Expert - we used Random Forests as a classification approach. Furthermore, we used a Support Vector Machine classifier, in order to perform a result comparison. By applying these approaches on data from a real system, Let's Go, we obtained preliminary results that we consider positive, given the difficulty of the task and the lack of competing approaches for comparison.Comment: 10 page

Hierarchical Multi-Label Dialog Act Recognition on Spanish Data

Dialog acts reveal the intention behind the uttered words. Thus, their automatic recognition is important for a dialog system trying to understand its conversational partner. The study presented in this article approaches that task on the DIHANA corpus, whose three-level dialog act annotation scheme poses problems which have not been explored in recent studies. In addition to the hierarchical problem, the two lower levels pose multi-label classification problems. Furthermore, each level in the hierarchy refers to a different aspect concerning the intention of the speaker both in terms of the structure of the dialog and the task. Also, since its dialogs are in Spanish, it allows us to assess whether the state-of-the-art approaches on English data generalize to a different language. More specifically, we compare the performance of different segment representation approaches focusing on both sequences and patterns of words and assess the importance of the dialog history and the relations between the multiple levels of the hierarchy. Concerning the single-label classification problem posed by the top level, we show that the conclusions drawn on English data also hold on Spanish data. Furthermore, we show that the approaches can be adapted to multi-label scenarios. Finally, by hierarchically combining the best classifiers for each level, we achieve the best results reported for this corpus.Comment: 21 pages, 4 figures, 17 tables, translated version of the article published in Linguam\'atica 11(1

Otimização simultânea de forma e topologia de estruturas casca

Neste trabalho aplica-se o modelo desenvolvido por Hassani et al. (2013) para a otimização simultânea de forma e topologia de estruturas casca, fazendo uso do Método das Assimptotas Móveis (MMA). Grande parte dos estudos e projetos de otimização estrutural realizados consideram a otimização de forma e topologia como dois processos separados. Ou seja, numa primeira etapa é definido o layout de material e de seguida a geometria ótima. Mas, esta dissertação tem por objetivo explorar a otimização simultânea de forma e topologia, onde o layout de material e a geometria são otimizados em simultâneo, aplicando conceitos abordados por Hassani et al. (2013). Para os problemas abordados aqui, o objetivo foi a maximização da rigidez de uma estrutura casca com uma restrição sobre o volume total da estrutura. Para tal: • Foram utilizadas superfícies B-Splines para modelar e controlar a geometria das estruturas casca; • O software Abaqus foi utilizado para a implementação do método dos elementos finitos; • Foi considerado o modelo de material Solid Isotropic Material with Penalty (SIMP) para a otimização de topologia; • O MMA foi aplicado para os processos de otimização e sendo este um método baseado nos gradientes, foi realizada uma análise de sensibilidades para obter as derivadas da função objetivo e das restrições de projeto; • De forma a evitar/aliviar o efeito das instabilidades associadas à otimização de topologia (dependência de malha, checkerboard e mínimos locais) foi aplicado o método da convolução. Alguns exemplos (retirados da literatura) são aplicados ao longo da dissertação (topologia, forma e por fim forma & topologia) para verificar o desempenho do método aplicado

rPrism: a software for reactive weighted state transition models

In this work we introduce the software rPrism, as a branch of the software PRISM model checker, in order to be able to study weighted reactive state transition models. This kind of model gathers together the concepts of reactivity { which consists of the capacity of a state transition model to alter its accessibility relation { and weights, which can be seen as costs, rates, etc.. Given a speci c model, the tool performs a simulation based on a Continuous Time Markov Chain. In particular, we show an example of its application for biological systems.This work was supported by ERDF - The European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme and by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia, within project POCI-01-0145-FEDER-030947 and project with reference UID/MAT/04106/2019 at CIDMA. The authors acknowledge the support given by a France-Portugal partnership PHC PESSOA 2018 between M. Chaves (Campus France #40823SD) and M. A. Martins. D. Figueiredo also acknowledges the support given by FCT via the PhD scholarship PD/BD/114186/2016.publishe