The costs of illiteracy in South Africa

In South Africa there has been a surge in publicly funded adult literacy education in recent years. There is a recognition that for the effective monitoring of adult literacy, direct measures of literacy are required. Grade attainment, self-reported ability to read and behavioural variables relating to, for instance, reading habits produce vastly different measures of adult literacy in South Africa. It is noteworthy that self-reported values change over time as people’s perceptions of what consitutes literacy shifts. A 75% literacy rate is arguably a plausible figure, though the absence of a direct measure is problematic. An education production function suggests that literacy-related parent behaviour, independently of parent years of education, influences performance of learners in school. In a multivariate employment model, self-reported literacy is a statistically significant predictor of being employed. In a cross-country growth model, poor quality schooling emerges as the variable requiring the most urgent policy attention to sustain and improve South Africa’s economic development. Both microeconomic and macroeconomic estimates suggest that with a more typical level of school performance South Africa’s GDP would be 23% to 30% higher than it currently is.Literacy, Illiteracy, South Africa, Education production function, Economic growth

Towards a Robuster Interpretive Parsing

The input data to grammar learning algorithms often consist of overt forms that do not contain full structural descriptions. This lack of information may contribute to the failure of learning. Past work on Optimality Theory introduced Robust Interpretive Parsing (RIP) as a partial solution to this problem. We generalize RIP and suggest replacing the winner candidate with a weighted mean violation of the potential winner candidates. A Boltzmann distribution is introduced on the winner set, and the distribution’s parameter $T$ is gradually decreased. Finally, we show that GRIP, the Generalized Robust Interpretive Parsing Algorithm significantly improves the learning success rate in a model with standard constraints for metrical stress assignment

A hybrid algorithm for Bayesian network structure learning with application to multi-label learning

We present a novel hybrid algorithm for Bayesian network structure learning, called H2PC. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges. The algorithm is based on divide-and-conquer constraint-based subroutines to learn the local structure around a target variable. We conduct two series of experimental comparisons of H2PC against Max-Min Hill-Climbing (MMHC), which is currently the most powerful state-of-the-art algorithm for Bayesian network structure learning. First, we use eight well-known Bayesian network benchmarks with various data sizes to assess the quality of the learned structure returned by the algorithms. Our extensive experiments show that H2PC outperforms MMHC in terms of goodness of fit to new data and quality of the network structure with respect to the true dependence structure of the data. Second, we investigate H2PC's ability to solve the multi-label learning problem. We provide theoretical results to characterize and identify graphically the so-called minimal label powersets that appear as irreducible factors in the joint distribution under the faithfulness condition. The multi-label learning problem is then decomposed into a series of multi-class classification problems, where each multi-class variable encodes a label powerset. H2PC is shown to compare favorably to MMHC in terms of global classification accuracy over ten multi-label data sets covering different application domains. Overall, our experiments support the conclusions that local structural learning with H2PC in the form of local neighborhood induction is a theoretically well-motivated and empirically effective learning framework that is well suited to multi-label learning. The source code (in R) of H2PC as well as all data sets used for the empirical tests are publicly available.Comment: arXiv admin note: text overlap with arXiv:1101.5184 by other author

Learning a Policy for Opportunistic Active Learning

Active learning identifies data points to label that are expected to be the most useful in improving a supervised model. Opportunistic active learning incorporates active learning into interactive tasks that constrain possible queries during interactions. Prior work has shown that opportunistic active learning can be used to improve grounding of natural language descriptions in an interactive object retrieval task. In this work, we use reinforcement learning for such an object retrieval task, to learn a policy that effectively trades off task completion with model improvement that would benefit future tasks.Comment: EMNLP 2018 Camera Read