Evaluation of the NLP Components of the OVIS2 Spoken Dialogue System

The NWO Priority Programme Language and Speech Technology is a 5-year research programme aiming at the development of spoken language information systems. In the Programme, two alternative natural language processing (NLP) modules are developed in parallel: a grammar-based (conventional, rule-based) module and a data-oriented (memory-based, stochastic, DOP) module. In order to compare the NLP modules, a formal evaluation has been carried out three years after the start of the Programme. This paper describes the evaluation procedure and the evaluation results. The grammar-based component performs much better than the data-oriented one in this comparison.Comment: Proceedings of CLIN 9

Cooperative Synchronization in Wireless Networks

Synchronization is a key functionality in wireless network, enabling a wide variety of services. We consider a Bayesian inference framework whereby network nodes can achieve phase and skew synchronization in a fully distributed way. In particular, under the assumption of Gaussian measurement noise, we derive two message passing methods (belief propagation and mean field), analyze their convergence behavior, and perform a qualitative and quantitative comparison with a number of competing algorithms. We also show that both methods can be applied in networks with and without master nodes. Our performance results are complemented by, and compared with, the relevant Bayesian Cram\'er-Rao bounds

A theory of minimal updates in holography

Consider two quantum critical Hamiltonians $H$ and $\tilde{H}$ on a $d$-dimensional lattice that only differ in some region $\mathcal{R}$. We study the relation between holographic representations, obtained through real-space renormalization, of their corresponding ground states $\left.| \psi \right\rangle$ and $\left.| \tilde{\psi} \right\rangle$. We observe that, even though $\left.| \psi \right\rangle$ and $\left.| \tilde{\psi} \right\rangle$ disagree significantly both inside and outside region $\mathcal{R}$, they still admit holographic descriptions that only differ inside the past causal cone $\mathcal{C}(\mathcal{R})$ of region $\mathcal{R}$, where $\mathcal{C}(\mathcal{R})$ is obtained by coarse-graining region $\mathcal{R}$. We argue that this result follows from a notion of directed influence in the renormalization group flow that is closely connected to the success of Wilson's numerical renormalization group for impurity problems. At a practical level, directed influence allows us to exploit translation invariance when describing a homogeneous system with e.g. an impurity, in spite of the fact that the Hamiltonian is no longer invariant under translations.Comment: main text: 5 pages, 4 figures, appendices: 7 pages, 7 figures. Revised for greater clarit

Viterbi Training for PCFGs: Hardness Results and Competitiveness of Uniform Initialization

We consider the search for a maximum likelihood assignment of hidden derivations and grammar weights for a probabilistic context-free grammar, the problem approximately solved by “Viterbi training.” We show that solving and even approximating Viterbi training for PCFGs is NP-hard. We motivate the use of uniformat-random initialization for Viterbi EM as an optimal initializer in absence of further information about the correct model parameters, providing an approximate bound on the log-likelihood.