Domain Agnostic Real-Valued Specificity Prediction

Sentence specificity quantifies the level of detail in a sentence, characterizing the organization of information in discourse. While this information is useful for many downstream applications, specificity prediction systems predict very coarse labels (binary or ternary) and are trained on and tailored toward specific domains (e.g., news). The goal of this work is to generalize specificity prediction to domains where no labeled data is available and output more nuanced real-valued specificity ratings. We present an unsupervised domain adaptation system for sentence specificity prediction, specifically designed to output real-valued estimates from binary training labels. To calibrate the values of these predictions appropriately, we regularize the posterior distribution of the labels towards a reference distribution. We show that our framework generalizes well to three different domains with 50%~68% mean absolute error reduction than the current state-of-the-art system trained for news sentence specificity. We also demonstrate the potential of our work in improving the quality and informativeness of dialogue generation systems.Comment: AAAI 2019 camera read

ATTENTION AND SCHOOL SUCCESS: The Long-Term Implications of Attention for School Success among Low-Income Children

This study examined the longitudinal associations between sustained attention in preschool and children’s school success in later elementary school within a low-income sample (N = 2,403). Specifically, two facets of sustained attention (focused attention and lack of impulsivity) at age 5 were explored as independent predictors of children’s academic and behavioral competence across eight measures at age 9. Overall, the pattern of results indicates specificity between the facets of attention and school success, such that focused attention was primarily predictive of academic outcomes while impulsivity was mainly predictive of behavioral outcomes. Both facets of attention predicted teacher ratings of children’s academic skills and approaches to learning, which suggests that they jointly influence outcomes that span both domains of school success. Patterns of association were similar for children above and below the poverty line. Implications of these findings for interventions targeting school readiness and success among at-risk children are discussed.sustained attention, academic achievement, behavioral competence, low-income children

Children's acquisition of science terms: does fast mapping work?

About the book: This proceedings contains 99 selected papers from the 8th Conference of the International Association for the Study of Child Language (IASCL) held in Donostia-San Sebastián in the Spanish Basque Country in July 1999. The proceedings includes the plenary addresses by Jean-Paul Bronckart, Brian MacWhinney, and Miquel Siguan. The other 96 papers are organized into sections on bilingualism, discourse, phonology, language disorders, lexicon, morphology, syntax, and signed languages. Several of these sections include symposia with introductions as well as individual papers

Implementing Observation Protocols: Lessons for K-12 Education From the Field of Early Childhood

Examines issues for implementing standardized observation protocols for teacher evaluations. Makes recommendations based on lessons from preschool, such as the need to show empirical links between teacher performance and student learning and development

Learning Tasks for Multitask Learning: Heterogenous Patient Populations in the ICU

Machine learning approaches have been effective in predicting adverse outcomes in different clinical settings. These models are often developed and evaluated on datasets with heterogeneous patient populations. However, good predictive performance on the aggregate population does not imply good performance for specific groups. In this work, we present a two-step framework to 1) learn relevant patient subgroups, and 2) predict an outcome for separate patient populations in a multi-task framework, where each population is a separate task. We demonstrate how to discover relevant groups in an unsupervised way with a sequence-to-sequence autoencoder. We show that using these groups in a multi-task framework leads to better predictive performance of in-hospital mortality both across groups and overall. We also highlight the need for more granular evaluation of performance when dealing with heterogeneous populations.Comment: KDD 201