Equal access to community interpreting in Flanders: a matter of self-reflective decision making?

The central issue tackled by this article revolves around decision-making by public service institutions in respect of the uses and perceived effects of community interpreting and translation in Flanders (Belgium) and/or other resources of language support (such as the use of a lingua franca, soliciting the help of a multilingual co-worker, etc.). The aim of the study was to obtain a clear understanding of processes and participant frameworks of decision-making with regard to language support within these institutions. Consequently, a qualitative survey was set up focusing on three selected geographic regions and, within these regions, on four domains of public service (health, education, public administration and employment assistance). Respondents were both institutional end users and immigrants. The results reveal a lack of national and local organizational policy and explicit procedures in the allocation of language support resources. This lack contributes to inequality in foreign language users’ access to the services of public institutions. It is recommended that a self-reflective framework be introduced for regulating access to a more systematic use of community interpreting alongside other instruments or strategies for bridging language barriers. Such a framework should be tailored to the needs of the institution’s clients and to domain-specific and local needs of the institution. It should also include the relative availability of other adequate instruments for bridging language barriers

The role of domain-general cognitive control in language comprehension

What role does domain-general cognitive control play in understanding linguistic input? Although much evidence has suggested that domain-general cognitive control and working memory resources are sometimes recruited during language comprehension, many aspects of this relationship remain elusive. For example, how frequently do cognitive control mechanisms get engaged when we understand language? And is this engagement necessary for successful comprehension? I here a) review recent brain imaging evidence for the neural separability of the brain regions that support high-level linguistic processing vs. those that support domain-general cognitive control abilities; b) define the space of possibilities for the relationship between these sets of brain regions; and c) review the available evidence that constrains these possibilities to some extent. I argue that we should stop asking whether domain-general cognitive control mechanisms play a role in language comprehension, and instead focus on characterizing the division of labor between the cognitive control brain regions and the more functionally specialized language regions

Spatial evolution of human dialects

The geographical pattern of human dialects is a result of history. Here, we formulate a simple spatial model of language change which shows that the final result of this historical evolution may, to some extent, be predictable. The model shows that the boundaries of language dialect regions are controlled by a length minimizing effect analogous to surface tension, mediated by variations in population density which can induce curvature, and by the shape of coastline or similar borders. The predictability of dialect regions arises because these effects will drive many complex, randomized early states toward one of a smaller number of stable final configurations. The model is able to reproduce observations and predictions of dialectologists. These include dialect continua, isogloss bundling, fanning, the wave-like spread of dialect features from cities, and the impact of human movement on the number of dialects that an area can support. The model also provides an analytical form for S\'{e}guy's Curve giving the relationship between geographical and linguistic distance, and a generalisation of the curve to account for the presence of a population centre. A simple modification allows us to analytically characterize the variation of language use by age in an area undergoing linguistic change

Bibliography of Sources on Dena’ina and Cook Inlet Anthropology Through 2016

This version 4.3 will be the final version for this bibliography, a project that was begun in 1993 by Greg Dixon. We have intentionally excluded all potential references for the year 2017. This version is about 29 pages longer and has about 211 entries added since the previous version 3.1 of 2012. Aaron Leggett has added over fifty sources many being rare items from newpapers and magazines. Also many corrections and additions were made to entries in earlier versions.I wish to thank Kenaitze Indian Tribe and the “Dena’ina Language Revitalization Project” for their support for several projects during 2017-2018, including this Vers. 4.3. Previous versions have had partial support from "Dena'ina Archiving, Training and Access" project (NSF-OPP 0326805, 2004) and from Lake Clark National Park. I thank Katherine Arndt of Alaska & Polar Regions at UAF for her careful proofreading

Assessment and diagnosis of Developmental Language Disorder: The experiences of speech and language therapists

© The Author(s) 2019. This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).Background: For many years research and practice have noted the impact of the heterogeneous nature of Developmental Language Disorder (also known as language impairment or specific language impairment) on diagnosis and assessment. Recent research suggests the disorder is not restricted to the language domain and against this background, the challenge for the practitioner is to provide accurate assessment and effective therapy. The language practitioner aims to support the child and their carers to achieve the best outcomes. However, little is known about the experiences of the language practitioner in the assessment process, in contrast to other childhood disorders, yet their expertise is central in the assessment and diagnosis of children with language disorder. Aims: This study aimed to provide a detailed qualitative description of the experiences of speech and language therapists involved in the assessment and diagnosis of children with Developmental Language Disorder. Methods & Procedures: The qualitative study included three focus groups to provide a credible and rich description of the experiences of speech and language therapists involved in the assessment of Developmental Language Disorder. The speech and language therapists who participated in the study were recruited from three NHS Trusts across the UK and all were directly involved in the assessment and diagnosis procedures. The lengths of practitioner experience ranged from 2 years to 38 years. The data was analysed using a thematic analysis in accordance with the principles set out by Braun & Clarke (2006). Outcomes & Results: The data showed a number of key themes concerning the experiences of speech and language therapists in assessing children with Developmental Language Disorder (DLD). These themes ranged from the participants’ experiences of the barriers to early referral, challenges for assessment and the concerns over continued future support. Conclusions & Implications: This study provides first-hand evidence from speech and language therapists in the assessment of children with Developmental Language Disorder, drawing together experiences from language practitioners from different regions. The findings provide insight to the barriers to referral, the potential variations in the assessment process, the role of practitioner expertise and the challenges faced them. The importance of early intervention, useful assessment tools and future support were expressed. Taken together, the results relate to some issues to be addressed on a practical level and a continuing need for initiatives to raise awareness of DLD in the public domain.Peer reviewe

Brain bases of morphological processing in young children

How does the developing brain support the transition from spoken language to print? Two spoken language abilities form the initial base of child literacy across languages: knowledge of language sounds (phonology) and knowledge of the smallest units that carry meaning (morphology). While phonology has received much attention from the field, the brain mechanisms that support morphological competence for learning to read remain largely unknown. In the present study, young English‐speaking children completed an auditory morphological awareness task behaviorally (n = 69, ages 6–12) and in fMRI (n = 16). The data revealed two findings: First, children with better morphological abilities showed greater activation in left temporoparietal regions previously thought to be important for supporting phonological reading skills, suggesting that this region supports multiple language abilities for successful reading acquisition. Second, children showed activation in left frontal regions previously found active in young Chinese readers, suggesting morphological processes for reading acquisition might be similar across languages. These findings offer new insights for developing a comprehensive model of how spoken language abilities support children's reading acquisition across languages. Hum Brain Mapp 36:2890–2900, 2015. © 2015 Wiley Periodicals, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/112232/1/hbm22815.pd

HSTREAM: A directive-based language extension for heterogeneous stream computing

Big data streaming applications require utilization of heterogeneous parallel computing systems, which may comprise multiple multi-core CPUs and many-core accelerating devices such as NVIDIA GPUs and Intel Xeon Phis. Programming such systems require advanced knowledge of several hardware architectures and device-specific programming models, including OpenMP and CUDA. In this paper, we present HSTREAM, a compiler directive-based language extension to support programming stream computing applications for heterogeneous parallel computing systems. HSTREAM source-to-source compiler aims to increase the programming productivity by enabling programmers to annotate the parallel regions for heterogeneous execution and generate target specific code. The HSTREAM runtime automatically distributes the workload across CPUs and accelerating devices. We demonstrate the usefulness of HSTREAM language extension with various applications from the STREAM benchmark. Experimental evaluation results show that HSTREAM can keep the same programming simplicity as OpenMP, and the generated code can deliver performance beyond what CPUs-only and GPUs-only executions can deliver.Comment: Preprint, 21st IEEE International Conference on Computational Science and Engineering (CSE 2018

Transfer Learning for Multi-language Twitter Election Classification

Both politicians and citizens are increasingly embracing social media as a means to disseminate information and comment on various topics, particularly during significant political events, such as elections. Such commentary during elections is also of interest to social scientists and pollsters. To facilitate the study of social media during elections, there is a need to automatically identify posts that are topically related to those elections. However, current studies have focused on elections within English-speaking regions, and hence the resultant election content classifiers are only applicable for elections in countries where the predominant language is English. On the other hand, as social media is becoming more prevalent worldwide, there is an increasing need for election classifiers that can be generalised across different languages, without building a training dataset for each election. In this paper, based upon transfer learning, we study the development of effective and reusable election classifiers for use on social media across multiple languages. We combine transfer learning with different classifiers such as Support Vector Machines (SVM) and state-of-the-art Convolutional Neural Networks (CNN), which make use of word embedding representations for each social media post. We generalise the learned classifier models for cross-language classification by using a linear translation approach to map the word embedding vectors from one language into another. Experiments conducted over two election datasets in different languages show that without using any training data from the target language, linear translations outperform a classical transfer learning approach, namely Transfer Component Analysis (TCA), by 80% in recall and 25% in F1 measure