The Consequences of Multicollinearity among Socioeconomic Predictors of Negative Concord in Philadelphia

This study is a reanalysis of the external predictors of the use of negative concord in Philadelphia, using archival data from the Language Change and Variation survey. It is shown that the interpretation of the effects of the various socioeconomic measures reported by Labov (2001) was biased by their multicollinearity and by per-subject differences. A new mixed-effects model with residualized socioeconomic predictors and a per-subject random intercept shows the predictive role of all four socioeconomic measures, and the per-subject estimates are used to identify the nascent leaders of linguistic change

Hierarchical regression modeling for language research

I demonstrate the application of hierarchical regression modeling, a state-of-the-art technique for statistical inference, to language research. First, a stable sociolinguistic variable in Philadelphia (Labov, 2001) is reconsidered, with attention paid to the treatment of collinearities among socioeconomic predictors. I then demonstrate the use of hierarchical models to account for the random sampling of subjects and items in an experimental setting, using data from a study of word-learning in the face of tonal variation (Quam and Swingley, forthcoming). The results from these case studies demonstrate that modeling sampling from the population has empirical consequences

Weird inflects but OK : Making sense of morphological generation errors

We conduct a manual error analysis of the CoNLL-SIGMORPHON 2017 Shared Task on Morphological Reinflection. In this task, systems are given a word in citation form (e.g., hug) and asked to produce the corresponding inflected form (e.g., the simple past hugged). This design lets us analyze errors much like we might analyze children's production errors. We propose an error taxonomy and use it to annotate errors made by the top two systems across twelve languages. Many of the observed errors are related to inflectional patterns sensitive to inherent linguistic properties such as animacy or affect; many others are failures to predict truly unpredictable inflectional behaviors. We also find nearly one quarter of the residual "errors" reflect errors in the gold data. © 2019 Association for Computational Linguistics.Peer reviewe

Community level digital mental health interventions:A policy and practice brief

The prevalence of mental ill-health is increasing worldwide and brings adverse consequences at both the individual and societal level. Treatments and interventions for the symptoms that represent mental health conditions may target biological, behavioural and cognitive factors. Traditionally, treatments have included psychotropic medication, and/or psychological therapies which are delivered on a one to one or group basis. Both have a high economic cost, and efficacy varies. In addition, help seeking behaviour is impacted by stigma, symptom recognition &amp; understanding, and a host of factors associated with the disorders themselves, such as avoidance behaviour. The delivery of face-to-face interventions for those who are most marginalised and most at risk from mental ill-health, can also be impacted by barriers, such as knowledge of the services available and time, connectivity or travel constraints. The research presented here is co-produced with service providers, end users and academic experts across the disciplines of psychology, business, medicine, healthcare, interaction design and computer science. This briefing is based on the findings from our research programme on a community level digital mental health intervention.<br/

Results of the Second SIGMORPHON Shared Task on Multilingual Grapheme-to-Phoneme Conversion

Grapheme-to-phoneme conversion is an important component in many speech technologies, but until recently there were no multilingual benchmarks for this task. The second iteration of the SIGMORPHON shared task on multilingual grapheme-to-phoneme conversion features many improvements from the previous year's task (Gorman et al. 2020), including additional languages, a stronger baseline, three subtasks varying the amount of available resources, extensive quality assurance procedures, and automated error analyses. Four teams submitted a total of thirteen systems, at best achieving relative reductions of word error rate of 11% in the high-resource subtask and 4% in the low-resource subtask

Subjective evaluation of experimental dyspnoea: effects of isocapnia and repeated exposure

Resistive respiratory loading is an established stimulus for the induction of experimental dyspnoea. In comparison to unloaded breathing, resistive loaded breathing alters end-tidal CO2 (PETCO2), which has independent physiological effects (e.g. upon cerebral blood flow). We investigated the subjective effects of resistive loaded breathing with stabilized PETCO2 (isocapnia) during manual control of inspired gases on varying baseline levels of mild hypercapnia increased PETCO2). Furthermore, to investigate whether perceptual habituation to dyspnoea stimuli occurs, the study was repeated over four experimental sessions. Isocapnic hypercapnia did not affect dyspnoea unpleasantness during resistive loading. A post hoc analysis revealed a small increase of respiratory unpleasantness during unloaded breathing at +0.6 kPa, the level that reliably induced isocapnia. We didnot observe perceptual habituation over the four sessions. We conclude that isocapnic respiratory loading allows stable induction of respiratory unpleasantness, making it a good stimulus for multi-session studies of dyspnoea