This study examines knowledge sharing in Catholic organizations. The authors adopt Schein's organizational culture theory that facilitates, or inhibits, knowledge sharing in organizations. Thus, they address the phenomenon at the three levels: the artifacts, the norms and values, and the underlying assumptions. Considering the chosen settings, they study the contributions of individuals having taken vows, the organizational rituals, the significance, and the sense of community perceived by the organizational members. Data were gathered using a survey and were analyzed by using a fuzzy-set qualitative comparative analysis. The study provides the causal configurations of conditions that lead to tacit, explicit, and total knowledge sharing. They also offer the causal configurations of conditions that lead to the absence of each kind of knowledge sharing. Given that the qualitative results cannot be generalized, the study can still be replicated in organizations without restrictions.

This paper presents the Multilingual Artificial Intelligence Agent Assistant (MAIA), a project led by Unbabel with the collaboration of CMU, INESC-ID and IT Lisbon. MAIA will employ cutting-edge machine learning and natural language processing technologies to build multilingual AI agent assistants, eliminating language barriers. MAIA’s translation layer will empower human agents to provide customer support in real-time, in any language, with human quality.

Non-destructive testing (NDT) based on eddy currents (EC) is commonly used to detect defects in conductive materials. Usually the system includes an emitter coil, and one receiver coil or one Magnetoresistive (MR) sensor. In this work we added an interface ASIC that pre-amplifies and filters the signal from an array of MR sensors. This demo will present a new version based on the work presented at the ECNDT 2014 conference with a paper entitled “A CMOS ASIC for Precise Reading of a Magnetoresistive Sensor Array for NDT”. Since this is an on-going work, improvements have been made, namely the reduction of the system thermal noise to 30 nV/√Hz, the development of a multigain amplifier and the application of the same concept and circuit to a multichannel parallel signal acquisition system. Detection of surface and buried defects will be demonstrated in different material mock-ups.

We consider the problem of fully unsupervised learning of grammatical (part-of-speech) categories from unlabeled text. The standard maximum-likelihood hidden Markov model for this task performs poorly, because of its weak inductive bias and large model capacity. We address this problem by refining the model and modifying the learning objective to control its capacity via para- metric and non-parametric constraints. Our approach enforces word-category association sparsity, adds morphological and orthographic features, and eliminates hard-to-estimate parameters for rare words. We develop an efficient learning algorithm that is not much more computationally intensive than standard training. We also provide an open-source implementation of the algorithm. Our experiments on five diverse languages (Bulgarian, Danish, English, Portuguese, Spanish) achieve significant improvements compared with previous methods for the same task.

Word-level alignment of bilingual text is a critical resource for a growing variety of tasks. Probabilistic models for word alignment present a fundamental trade-off between richness of captured constraints and correlations versus efficiency and tractability of inference. In this article, we use the Posterior Regularization framework (Graça, Ganchev, and Taskar 2007) to incorporate complex constraints into probabilistic models during learning without changing the efficiency of the underlying model. We focus on the simple and tractable hidden Markov model, and present an efficient learning algorithm for incorporating approximate bijectivity and symmetry constraints. Models estimated with these constraints produce a significant boost in performance as measured by both precision and recall of manually annotated alignments for six language pairs. We also report experiments on two different tasks where word alignments are required: phrase-based machine translation and syntax transfer, and show promising improvements over standard methods.

In this paper we explore the problem of biasing unsupervised models to favor sparsity. We extend the posterior regularization framework [8] to encourage the model to achieve posterior sparsity on the unlabeled training data. We apply this new method to learn first-order HMMs for unsupervised part-of-speech (POS) tagging, and show that HMMs learned this way consistently and significantly out-performs both EM-trained HMMs, and HMMs with a sparsity-inducing Dirichlet prior trained by variational EM. We evaluate these HMMs on three languages — English, Bulgarian and Portuguese — under four conditions. We find that our method always improves performance with respect to both baselines, while variational Bayes actually degrades performance in most cases. We increase accuracy with respect to EM by 2.5%-8.7% absolute and we see improvements even in a semisupervised condition where a limited dictionary is provided.

The expectation maximization (EM) algorithm is a widely used maximum likelihood estimation procedure for statistical models when the values of some of the variables in the model are not observed. Very often, however, our aim is primarily to find a model that assigns values to the latent variables that have intended meaning for our data and maximizing expected likelihood only sometimes accomplishes this. Unfortunately, it is typically difficult to add even simple a-priori information about latent variables in graphical models without making the models overly complex or intractable. In this paper, we present an efficient, principled way to inject rich constraints on the posteriors of latent variables into the EM algorithm. Our method can be used to learn tractable graphical models that satisfy additional, otherwise intractable constraints. Focusing on clustering and the alignment problem for statistical machine translation, we show that simple, intuitive posterior constraints can greatly improve the performance over standard baselines and be competitive with more complex, intractable models.