Spectral decomposition method of dialog state tracking via collective matrix factorization

The task of dialog management is commonly decomposed into two sequential subtasks: dialog state tracking and dialog policy learning. In an end-to-end dialog system, the aim of dialog state tracking is to accurately estimate the true dialog state from noisy observations produced by the speech recognition and the natural language understanding modules. The state tracking task is primarily meant to support a dialog policy. From a probabilistic perspective, this is achieved by maintaining a posterior distribution over hidden dialog states composed of a set of context dependent variables. Once a dialog policy is learned, it strives to select an optimal dialog act given the estimated dialog state and a defined reward function. This paper introduces a novel method of dialog state tracking based on a bilinear algebric decomposition model that provides an efficient inference schema through collective matrix factorization. We evaluate the proposed approach on the second Dialog State Tracking Challenge (DSTC-2) dataset and we show that the proposed tracker gives encouraging results compared to the state-of-the-art trackers that participated in this standard benchmark. Finally, we show that the prediction schema is computationally efficient in comparison to the previous approaches

The Dialog State Tracking Challenge Series: A Review

In a spoken dialog system, dialog state tracking refers to the task of correctly inferring the state of the conversation -- such as the user's goal -- given all of the dialog history up to that turn.  Dialog state tracking is crucial to the success of a dialog system, yet until recently there were no common resources, hampering progress.  The Dialog State Tracking Challenge series of 3 tasks introduced the first shared testbed and evaluation metrics for dialog state tracking, and has underpinned three key advances in dialog state tracking: the move from generative to discriminative models; the adoption of discriminative sequential techniques; and the incorporation of the speech recognition results directly into the dialog state tracker.  This paper reviews this research area, covering both the challenge tasks themselves and summarizing the work they have enabled

Model-based Bayesian Reinforcement Learning for Dialogue Management

Reinforcement learning methods are increasingly used to optimise dialogue policies from experience. Most current techniques are model-free: they directly estimate the utility of various actions, without explicit model of the interaction dynamics. In this paper, we investigate an alternative strategy grounded in model-based Bayesian reinforcement learning. Bayesian inference is used to maintain a posterior distribution over the model parameters, reflecting the model uncertainty. This parameter distribution is gradually refined as more data is collected and simultaneously used to plan the agent's actions. Within this learning framework, we carried out experiments with two alternative formalisations of the transition model, one encoded with standard multinomial distributions, and one structured with probabilistic rules. We demonstrate the potential of our approach with empirical results on a user simulator constructed from Wizard-of-Oz data in a human-robot interaction scenario. The results illustrate in particular the benefits of capturing prior domain knowledge with high-level rules

Spectral decomposition method of dialog state tracking via collective matrix factorization

Revised versionThe task of dialog management is commonly decomposed into two sequential subtasks: dialog state tracking and dialog policy learning. In an end-to-end dialog system, the aim of dialog state tracking is to accurately estimate the true dialog state from noisy observations produced by the speech recognition and the natural language understanding modules. The state tracking task is primarily meant to support a dialog policy. From a probabilistic perspective, this is achieved by maintaining a posterior distribution over hidden dialog states composed of a set of context dependent variables. Once a dialog policy is learned, it strives to select an optimal dialog act given the estimated dialog state and a defined reward function. This paper introduces a novel method of dialog state tracking based on a bilinear algebric decomposition model that provides an efficient inference schema through collective matrix factorization. We evaluate the proposed approach on the second Dialog State Tracking Challenge (DSTC-2) dataset and we show that the proposed tracker gives encouraging results compared to the state-of-the-art trackers that participated in this standard benchmark. Finally, we show that the prediction schema is computationally efficient in comparison to the previous approaches

Challenges and opportunities for state tracking in statistical spoken dialog systems: results from two public deployments

Abstract-Whereas traditional dialog systems operate on the top ASR hypothesis, statistical dialog systems claim to be more robust to ASR errors by maintaining a distribution over multiple hidden dialog states. Recently, these techniques have been deployed publicly for the first time, making empirical measurements possible. In this paper, we analyze two of these deployments. We find that performance was quite mixed: in some cases statistical techniques improved accuracy with respect to the top speech recognition hypothesis; in other cases, accuracy was degraded. Investigating degradations, we find the three main causes are (non-obviously) inaccurate parameter estimates, poor confidence scores, and correlations in speech recognition errors. Overall the results suggest fundamental weaknesses in the formulation as a generative model, and we suggest alternatives as future work

Multi-population-based differential evolution algorithm for optimization problems

A differential evolution (DE) algorithm is an evolutionary algorithm for optimization problems over a continuous domain. To solve high dimensional global optimization problems, this work investigates the performance of differential evolution algorithms under a multi-population strategy. The original DE algorithm generates an initial set of suitable solutions. The multi-population strategy divides the set into several subsets. These subsets evolve independently and connect with each other according to the DE algorithm. This helps in preserving the diversity of the initial set. Furthermore, a comparison of combination of different mutation techniques on several optimization algorithms is studied to verify their performance. Finally, the computational results on the arbitrarily generated experiments, reveal some interesting relationship between the number of subpopulations and performance of the DE. Centralized charging of electric vehicles (EVs) based on battery swapping is a promising strategy for their large-scale utilization in power systems. In this problem, the above algorithm is designed to minimize total charging cost, as well as to reduce power loss and voltage deviation of power networks. The resulting algorithm and several others are executed on an IEEE 30-bus test system, and the results suggest that the proposed algorithm is one of effective and promising methods for optimal EV centralized charging

Essays on the Recombination and Diffusion of Innovations

RÉSUMÉ La mondialisation a réorganisé l'activité productive sur notre planète. Alors que les pays industrialisés étaient initialement le centre de l'activité manufacturière, ils ont perdu leur position auprès de pays émergents qui offrent des coûts de production moins élevés. Compte tenu de la quantité apparemment inépuisable de main-d'œuvre bon marché disponible à l'échelle mondiale, ce déplacement progressif des opérations de production ne semble pas avoir de fin en vue. Faisant face à ce sombre tableau de la situation économique, l'innovation technologique est considérée comme la panacée pour résoudre le problème de la croissance de la productivité et de la baisse du niveau de vie dans les économies avancées. Quelques mots de mise en garde doivent être dits contre de tels vœux pieux. Toutes les innovations technologiques n'ont pas le même impact économique et les récentes avancées technologiques ne semblent pas avoir le même impact que des innovations majeures du 19e siècle. De ce point de vue, le fait de ne pas contrôler le processus de production et de commercialisation des innovations qui ont une portée économique plus large semble être un obstacle pour ceux qui prêchent l'innovation comme une solution au problème de la stagnation économique. Du point de vue des cycles économiques, la croissance économique est enracinée dans la production d'innovations de bases. Ces percées servent de base à des inventions ultérieures dans une multitude de disciplines technologiques. Pourtant, malgré leur immense importance d'un point de vue social, on en sait peu sur les conditions qui conduisent à leur création et des bénéfices privés qu'elles engendrent pour les innovateurs. En ce qui concerne la question de la création d'innovations de base, l'importance de l'exploration technologique par rapport à l'exploitation est une source de débat. Les entreprises devraient-elles concentrer leurs efforts de recherche à un ensemble restreint de disciplines ou doivent-elles combiner des technologies distantes? En ce qui concerne la question sur les rendements privés sur l'innovation de base, le rôle des institutions publiques en tant que producteurs d'innovations de base est également un sujet controversé. L'objectif principal de cette thèse est de répondre à ces questions en identifiant 1) les conditions dans lesquelles la recombinaison de technologies distantes conduit à la propagation de l'invention résultante dans une multitude de disciplines, et 2) la manière dont les secteurs public et privé valorisent des innovations de base. Pour répondre à ces questions, des analyses économétriques d'un échantillon de brevets canadiens dans l'industrie de la nanotechnologie sont effectuées. En ce qui concerne la première question, les résultats montrent que la recombinaison distante conduit généralement à des innovations de base. Toutefois, un ensemble de modérateurs ont un impact sur la recombinaison distante. Alors que les organisations privées sont moins susceptibles de produire des innovations de base, leur effort pour combiner des technologies distantes est plus susceptible de produire des innovations de base. En outre, des liens forts avec les sciences fondamentales ont un effet négatif sur la recombinaison distante. En ce qui concerne la deuxième question de recherche, les résultats montrent que les innovations de base sont généralement associées à une perception des rendements privés plus importants sous conditions de dynamisme industriel et de régimes d'appropriation forts. Toutefois, en ce qui concerne les secteurs public et privé, les perceptions dépendent de la diffusion actuelle d'une technologie ainsi que sa diffusion future perçue. Les entreprises privées perçoivent des rendements plus élevés sur les inventions qui se sont déjà propagées dans plusieurs disciplines, tandis que celles qui seront propagées dans l'avenir sont perçues comme étant moins précieuses.----------ABSTRACT Globalization has reorganized productive activity in our planet. While industrialized countries where initially the center of manufacturing activity, they have lost their title to emerging economies who offer cheaper production costs. Given the seemingly endless supply of cheap labor available at a global level, this gradual shift of production operations does not appear to have an end in sight. In such a bleak economic picture, technological innovation is seen as the panacea for solving the problem of productivity growth, and thus the issue of decreasing standards of living in advanced economies. A few words of caution need to be said against such wishful thinking. All technological innovations do not have the same economic impact and recent technological advances do not appears to have the same impact as major innovations of the 19th century. From this perspective, the failure to control the process of producing and commercializing innovations that have broad economic impact appears to be an obstacle for those who preach innovation as a solution to the economic stagnation problem. From a business cycles perspective, economic growth is rooted in the production of basic innovations. These breakthroughs serve as the basis for subsequent inventions in a multitude of technological disciplines. Yet, despite their immense importance from a social point of view, little is known about the conditions that lead to their creation and the private benefits that they engender to innovators. Regarding the question about the creation of basic innovations, the importance of technological exploration versus exploitation is a major source of debate. When aiming for innovation impact, should firms focus their search effort to a focused set of disciplines or should they combine technologies from distant ones? Concerning the question about private returns to basic innovations, the role of public institutions as producers of basic innovations is also a controversial subject. The main purpose of this thesis is to answer these questions by identifying 1) the conditions under which distant technology recombination leads to the spread of the resulting invention across disciplines, and 2) how the private and public sectors value basic innovations. To answer these questions, econometric analyses of patenting activity in the Canadian nanotechnology industry are performed. Regarding the first question, the results show that distant recombination generally leads to basic innovations. However, a set of moderators have a negative impact on distant recombination. While private organizations are less likely to produce basic innovations, their effort to combine distant technologies is more likely to produce basic innovations. Also, strong linkage with basic science has a negative effect on distant recombination. Concerning the second research question, results show that basic innovations are generally associated with higher perceived private returns under conditions of industry dynamism and strong appropriability regimes. However, regarding private and public sectors, perceptions depend on the present spread of a technology and its future perceived spread. Firms perceives greater returns in inventions that have already spread across disciplines, while those that will subsequently spread in the future are perceived as less valuable

Discriminative methods for statistical spoken dialogue systems

Dialogue promises a natural and effective method for users to interact with and obtain information from computer systems. Statistical spoken dialogue systems are able to disambiguate in the presence of errors by maintaining probability distributions over what they believe to be the state of a dialogue. However, traditionally these distributions have been derived using generative models, which do not directly optimise for the criterion of interest and cannot easily exploit arbitrary information that may potentially be useful. This thesis presents how discriminative methods can overcome these problems in Spoken Language Understanding (SLU) and Dialogue State Tracking (DST). A robust method for SLU is proposed, based on features extracted from the full posterior distribution of recognition hypotheses encoded in the form of word confusion networks. This method uses discriminative classifiers, trained on unaligned input/output pairs. Performance is evaluated on both an off-line corpus, and on-line in a live user trial. It is shown that a statistical discriminative approach to SLU operating on the full posterior ASR output distribution can substantially improve performance in terms of both accuracy and overall dialogue reward. Furthermore, additional gains can be obtained by incorporating features from the system's output. For DST, a new word-based tracking method is presented that maps directly from the speech recognition results to the dialogue state without using an explicit semantic decoder. The method is based on a recurrent neural network structure that is capable of generalising to unseen dialogue state hypotheses, and requires very little feature engineering. The method is evaluated in the second and third Dialog State Tracking Challenges, as well as in a live user trial. The results demonstrate consistently high performance across all of the off-line metrics and a substantial increase in the quality of the dialogues in the live trial. The proposed method is shown to be readily applied to expanding dialogue domains, by exploiting robust features and a new method for online unsupervised adaptation. It is shown how the neural network structure can be adapted to output structured joint distributions, giving an improvement over estimating the dialogue state as a product of marginal distributions

Phylogeny-Aware Placement and Alignment Methods for Short Reads

In recent years bioinformatics has entered a new phase: New sequencing methods, generally referred to as Next Generation Sequencing (NGS) have become widely available. This thesis introduces algorithms for phylogeny aware analysis of short sequence reads, as generated by NGS methods in the context of metagenomic studies. A considerable part of this work focuses on the technical (w.r.t. performance) challenges of these new algorithms, which have been developed specifically to exploit parallelism