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With nearly a quarter of the world's population, members of at least five major language families plus several putative language isolates, South Asia is a fascinating arena for linguistic investigations, whether comparative-historical linguistics, studies of language contact and multilingualism, or general linguistic theory. This volume provides a state-of-the-art survey of linguistic research on the languages of South Asia, with contributions by well-known experts. Focus is both on what has been accomplished so far and on what remains unresolved or controversial and hence offers challenges for future research. In addition to covering the languages, their histories, and their genetic classification, as well as phonetics/phonology, morphology, syntax, and sociolinguistics, the volume provides special coverage of contact and convergence, indigenous South Asian grammatical traditions, applications of modern technology to South Asian languages, and South Asian writing systems. An appendix offers a classified listing of major sources and resources, both digital/online and printed.

This paper describes the development of a multilingual, manually annotated dataset for three under-resourced Dravidian languages generated from social media comments. The dataset was annotated for sentiment analysis and offensive language identification for a total of more than 60,000 YouTube comments. The dataset consists of around 44,000 comments in Tamil-English, around 7000 comments in Kannada-English, and around 20,000 comments in Malayalam-English. The data was manually annotated by volunteer annotators and has a high inter-annotator agreement in Krippendorff’s alpha. The dataset contains all types of code-mixing phenomena since it comprises user-generated content from a multilingual country. We also present baseline experiments to establish benchmarks on the dataset using machine learning and deep learning methods. The dataset is available on Github and Zenodo.

The poor often behave in less capable ways, which can further perpetuate poverty. We hypothesize that poverty directly impedes cognitive function and present two studies that test this hypothesis. First, we experimentally induced thoughts about finances and found that this reduces cognitive performance among poor but not in well-off participants. Second, we examined the cognitive function of farmers over the planting cycle. We found that the same farmer shows diminished cognitive performance before harvest, when poor, as compared with after harvest, when rich. This cannot be explained by differences in time available, nutrition, or work effort. Nor can it be explained with stress: Although farmers do show more stress before harvest, that does not account for diminished cognitive performance. Instead, it appears that poverty itself reduces cognitive capacity. We suggest that this is because poverty-related concerns consume mental resources, leaving less for other tasks. These data provide a previously unexamined perspective and help explain a spectrum of behaviors among the poor. We discuss some implications for poverty policy.

The interesting aspect of the Dravidian languages is a commonality through a shared script, similar vocabulary, and their common root language. In this work, an attempt has been made to classify the four complex Dravidian languages using cepstral coefficients and prosodic features. The speech of Dravidian languages has been recorded in various environments and considered as a database. It is demonstrated that while cepstral coefficients can indeed identify the language correctly with a fair degree of accuracy, prosodic features are added to the cepstral coefficients to improve language identification performance. Legendre polynomial fitting and the principle component analysis (PCA) are applied on feature vectors to reduce dimensionality which further resolves the issue of time complexity. In the experiments conducted, it is found that using both cepstral coefficients and prosodic features, a language identification rate of around 87% is obtained, which is about 18% above the baseline system using Mel-frequency cepstral coefficients (MFCCs). It is observed from the results that the temporal variations and prosody are the important factors needed to be considered for the tasks of language identification.

Kannada, one of the Dravidian languages, is the official language of Karnataka state of India. There is a need for a test using Kannada words that can assess visual aspects of reading independently of syntactic and semantic knowledge. A test of reading rate in Kannada was developed following the design principles of the Wilkins Rate of Reading Test (RRT). Fifteen high-frequency bisyllabic Kannada words were selected. Children were recruited from state and private schools that used Kannada or English as the medium of instruction. A total of 799 children from Grade 2 to 9 participated in the study. Reading rate was measured using the English RRT and the Kannada version twice in immediate succession during the first session. In 85 children, measurements using the Kannada RRT were repeated after an interval of 15 days. Pearson product moment correlation between the two immediately successive tests was 0.95 for the Kannada RRT and 0.91 for the English RRT. The correlation for the tests separated by an interval of 15 days was 0.83. When Kannada was the medium of instruction, there was little difference between test scores for Kannada and English. When English was the medium of instruction, test scores were greater in English. Scores increased as expected with age (P < 0.0001), similarly for Kannada and English tests. The newly developed Kannada RRT is both reliable and valid and can be used as a tool for measuring the visual aspects of reading.

Recent work on language technology has tried to recognize abusive language such as those containing hate speech and cyberbullying and enhance offensive language identification to moderate social media platforms. Most of these systems depend on machine learning models using a tagged dataset. Such models have been successful in detecting and eradicating negativity. However, an additional study has lately been conducted on the enhancement of free expression through social media. Instead of eliminating ostensibly unpleasant words, we created a multilingual dataset to recognize and encourage positivity in the comments, and we propose a novel custom deep network architecture, which uses a concatenation of embedding from T5-Sentence. We have experimented with multiple machine learning models, including SVM, logistic regression, K-nearest neighbour, decision tree, logistic neighbours, and we propose new CNN based model. Our proposed model outperformed all others with a macro F1-score of 0.75 for English, 0.62 for Tamil, and 0.67 for Malayalam.

While it is well-known that local anaphors match their antecedents in ϕ -features in many languages, it has been suggested that the form of anaphors is insensitive to the morphological case of their antecedent. We show that this is not the case for local complex reflexives (and reciprocals) in Telugu. Pieces of these elements must match in case features with their antecedents. We provide the first in-depth description and analysis of this type of reflexive. Our analysis bears on the structure of complex anaphors, the relation between anaphors and intensifiers in some languages, and the syntactic mechanisms that allow feature sharing.

Child characteristics, family factors, and preschool factors are all found to affect the rate of bilingual children's vocabulary development in heritage language (HL). However, what remains unknown is the relative importance of these three sets of factors in HL vocabulary growth. The current study explored the complex issue with 457 Singaporean preschool children who are speaking either Mandarin, Malay, or Tamil as their HL. A series of internal factors (e.g., non-verbal intelligence) and external factors (e.g., maternal educational level) were used to predict children's HL vocabulary growth over a year at preschool with linear mixed effects models. The results demonstrated that external factors (i.e., family and preschool factors) are relatively more important than child characteristics in enhancing bilingual children's HL vocabulary growth. Specifically, children's language input quantity (i.e., home language dominance), input quality (e.g., number of books in HL), and HL input quantity at school (i.e., the time between two waves of tests at preschool) predict the participants’ HL vocabulary growth, with initial vocabulary controlled. The relative importance of external factors in bilingual children's HL vocabulary development is attributed to the general bilingual setting in Singapore, where HL is taken as a subject to learn at preschool and children have fairly limited exposure to HL in general. The limited amount of input might not suffice to trigger the full expression of internal resources. Our findings suggest the crucial roles that caregivers and preschools play in early HL education, and the necessity of more parental involvement in early HL learning in particular.