Using Achievement Tests to Measure Language Assimilation and Language Bias among Immigrant Children

We use Woodcock Johnson III child assessment data in the New Immigrant Survey to examine language assimilation and test score bias among children of Hispanic immigrants. Our identification strategy exploits the test language randomization (Spanish or English) to quantitatively measure the degree and speed of language assimilation, in addition to the potential costs associated with taking a test in one’s non-dominant language. We find that U.S. born children of Hispanic immigrants are not bilingual as predicted by most language assimilation models but rather are English dominant. English language assimilation occurs at a rapid pace for foreign born children as well; children who arrive in the U.S. at an early age or who have spent more than four years in the U.S. do not benefit from taking the tests in Spanish. Results are robust to a fixed effects specification that controls for household level characteristics constant across siblings.immigration, language assimilation, New Immigrant Survey, Woodcock Johnson achievement tests

The role of automated feedback in training and retaining biological recorders for citizen science

The rapid rise of citizen science, with lay people forming often extensive biodiversity sensor networks, is seen as a solution to the mismatch between data demand and supply while simultaneously engaging citizens with environmental topics. However, citizen science recording schemes require careful consideration of how to motivate, train, and retain volunteers. We evaluated a novel computing science framework that allowed for the automated generation of feedback to citizen scientists using natural language generation (NLG) technology. We worked with a photo-based citizen science program in which users also volunteer species identification aided by an online key. Feedback is provided after photo (and identification) submission and is aimed to improve volunteer species identification skills and to enhance volunteer experience and retention. To assess the utility of NLG feedback, we conducted two experiments with novices to assess short-term (single session) and longer-term (5 sessions in 2 months) learning, respectively. Participants identified a specimen in a series of photos. One group received only the correct answer after each identification, and the other group received the correct answer and NLG feedback explaining reasons for misidentification and highlighting key features that facilitate correct identification. We then developed an identification training tool with NLG feedback as part of the citizen science program BeeWatch and analyzed learning by users. Finally, we implemented NLG feedback in the live program and evaluated this by randomly allocating all BeeWatch users to treatment groups that received different types of feedback upon identification submission. After 6 months separate surveys were sent out to assess whether views on the citizen science program and its feedback differed among the groups. Identification accuracy and retention of novices were higher for those who received automated feedback than for those who received only confirmation of the correct identification without explanation. The value of NLG feedback in the live program, captured through questionnaires and evaluation of the online photo-based training tool, likewise showed that the automated generation of informative feedback fostered learning and volunteer engagement and thus paves the way for productive and long-lived citizen science projects

Early Academic Performance in Children with Cleft Lip and/or Palate.

Studies of preschool children have shown early speech and language deficits in children with cleft lip and/or palate (CLP). For some children, the deficits during kindergarten diminish as they begin school while some children continue to show delays. The purpose of this study was to determine if a relationship exists between speech and language skills and early reading skills of phonological awareness, letter identification, and rapid naming in children with and without CLP. The subjects, four kindergarten children with and four without CLP, were administered a battery of speech, language, early reading skills, and nonverbal cognition measures. Two-way analysis of variance for groups and matched pairs and correlational analyses were performed. The results revealed that the cleft group performed poorer than the noncleft group on most of the speech, language, and early reading measures. Significant correlations were found between the speech and grammatical language measures and the early reading measures

Rapid Statistical Learning Supporting Word Extraction From Continuous Speech.

The identification of words in continuous speech, known as speech segmentation, is a critical early step in language acquisition. This process is partially supported by statistical learning, the ability to extract patterns from the environment. Given that speech segmentation represents a potential bottleneck for language acquisition, patterns in speech may be extracted very rapidly, without extensive exposure. This hypothesis was examined by exposing participants to continuous speech streams composed of novel repeating nonsense words. Learning was measured on-line using a reaction time task. After merely one exposure to an embedded novel word, learners demonstrated significant learning effects, as revealed by faster responses to predictable than to unpredictable syllables. These results demonstrate that learners gained sensitivity to the statistical structure of unfamiliar speech on a very rapid timescale. This ability may play an essential role in early stages of language acquisition, allowing learners to rapidly identify word candidates and break in to an unfamiliar language

The Impact of Word Representations on Sequential Neural MWE Identification

International audienceRecent initiatives such as the PARSEME shared task have allowed the rapid development of MWE identification systems. Many of those are based on recent NLP advances, using neural sequence models that take continuous word representations as input. We study two related questions in neural verbal MWE identification: (a) the use of lemmas and/or surface forms as input features, and (b) the use of word-based or character-based em-beddings to represent them. Our experiments on Basque, French, and Polish show that character-based representations yield systematically better results than word-based ones. In some cases, character-based representations of surface forms can be used as a proxy for lem-mas, depending on the morphological complexity of the language

I calchi linguistici nella lingua albanese. I calchi strutturali.

Il saggio prende in esame i calchi linguistici nella lingua albanese. Il rapido excursus degli studi sul calco si conclude focalizzando l’attenzione sulla lingua-modello, l’italiano, che ha costituito e costituisce per l’albanese il referente linguistico più importante cui attingere per la coniazione di numerosi neologismi. Tipologicamente lo studio si concentra sul calco strutturale e si articola in calchi strutturali perfetti e imperfetti. Ai fini pratici dell’indagine, queste categorie sono suddivise in calchi di derivazione e di composizione, considerati a loro volta analiticamente. Alla classificazione categoriale speculare nelle due lingue si affianca l’individuazione e la caratterizzazione delle differenze realizzative del calco nella lingua ricevente. L’interferenza ripetuta dell’italiano (prestito→calco) ha suggerito di enucleare una nuova tipologia di calco.The essay examines the linguistic calques in the Albanian language. The rapid survey of the studies on the calque ends focusing on model language, Italian, which has been and is the most important linguistic referent for the Albanian language in regards to the coinage of numerous neologisms. Typologically the study focuses on the structural calque with specific reference to perfect structural calque and imperfect structural calque. For practical purposes, these categories are divided into derivational calque and compositional calque, both studied analytically. The specular categorical classification, in the two languages, is accompanied by the identification and characterization of the differences realization of the calque in the target language. The repeated interference of Italian (loan → calque) suggested the identification of a new type of calque

Language Delay in Patients with CLN2 Disease: Could It Support Earlier Diagnosis?

Neuronal ceroid lipofuscinosis type 2 (CLN2 disease) is a rare pediatric disorder associated with rapid neurodegeneration, and premature death in adolescence. An effective enzyme replacement therapy (cerliponase alfa) has been approved that can reduce this predictable neurological decline. The nonspecific early symptoms of CLN2 disease frequently delay diagnosis and appropriate management. Seizures are generally recognized as the first presenting symptom of CLN2 disease, but emerging data show that language delay may precede this. An improved understanding of language deficits in the earliest stage of CLN2 disease may support the early identification of patients. In this article, CLN2 disease experts examine how language development is affected by CLN2 disease in their clinical practices. The authors' experiences highlighted the timings of first words and first use of sentences, and language stagnation as key features of language deficits in CLN2 disease, and how deficits in language may be an earlier sign of the disease than seizures. Potential challenges in identifying early language deficits include assessing patients with other complex needs, and recognizing that a child's language abilities are not within normal parameters given the variability of language development in young children. CLN2 disease should be considered in children presenting with language delay and/or seizures to facilitate earlier diagnosis and access to treatment that can significantly reduce morbidity

R2-D2: ColoR-inspired Convolutional NeuRal Network (CNN)-based AndroiD Malware Detections

The influence of Deep Learning on image identification and natural language processing has attracted enormous attention globally. The convolution neural network that can learn without prior extraction of features fits well in response to the rapid iteration of Android malware. The traditional solution for detecting Android malware requires continuous learning through pre-extracted features to maintain high performance of identifying the malware. In order to reduce the manpower of feature engineering prior to the condition of not to extract pre-selected features, we have developed a coloR-inspired convolutional neuRal networks (CNN)-based AndroiD malware Detection (R2-D2) system. The system can convert the bytecode of classes.dex from Android archive file to rgb color code and store it as a color image with fixed size. The color image is input to the convolutional neural network for automatic feature extraction and training. The data was collected from Jan. 2017 to Aug 2017. During the period of time, we have collected approximately 2 million of benign and malicious Android apps for our experiments with the help from our research partner Leopard Mobile Inc. Our experiment results demonstrate that the proposed system has accurate security analysis on contracts. Furthermore, we keep our research results and experiment materials on http://R2D2.TWMAN.ORG.Comment: Verison 2018/11/15, IEEE BigData 2018, Seattle, WA, USA, Dec 10-13, 2018. (Accepted