388 research outputs found

    DravidianCodeMix: Sentiment Analysis and Offensive Language Identification Dataset for Dravidian Languages in Code-Mixed Text

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    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 7,000 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 methods. The dataset is available on Github (https://github.com/bharathichezhiyan/DravidianCodeMix-Dataset) and Zenodo (https://zenodo.org/record/4750858\#.YJtw0SYo\_0M).Comment: 36 page

    Development of Multilingual Resource Management Mechanisms for Libraries

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    Multilingual is one of the important concept in any library. This study is create on the basis of global recommendations and local requirement for each and every libraries. Select the multilingual components for setting up the multilingual cluster in different libraries to each user. Development of multilingual environment for accessing and retrieving the library resources among the users as well as library professionals. Now, the methodology of integration of Google Indic Transliteration for libraries have follow the five steps such as (i) selection of transliteration tools for libraries (ii) comparison of tools for libraries (iii) integration Methods in Koha for libraries (iv) Development of Google indic transliteration in Koha for users (v) testing for libraries (vi) results for libraries. Development of multilingual framework for libraries is also an important task in integrated library system and in this section have follow the some important steps such as (i) Bengali Language Installation in Koha for libraries (ii) Settings Multilingual System Preferences in Koha for libraries (iii) Translate the Modules for libraries (iv) Bengali Interface in Koha for libraries. Apart from these it has also shows the Bengali data entry process in Koha for libraries such as Data Entry through Ibus Avro Phonetics for libraries and Data Entry through Virtual Keyboard for libraries. Development of Multilingual Digital Resource Management for libraries by using the DSpace and Greenstone. Management of multilingual for libraries in different areas such as federated searching (VuFind Multilingual Discovery tool ; Multilingual Retrieval in OAI-PMH tool ; Multilingual Data Import through Z39.50 Server ). Multilingual bibliographic data edit through MarcEditor for the better management of integrated library management system. It has also create and editing the content by using the content management system tool for efficient and effective retrieval of multilingual digital content resources among the users

    WLV-RIT at SemEval-2021 Task 5: A Neural Transformer Framework for Detecting Toxic Spans

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    In recent years, the widespread use of social media has led to an increase in the generation of toxic and offensive content on online platforms. In response, social media platforms have worked on developing automatic detection methods and employing human moderators to cope with this deluge of offensive content. While various state-of-the-art statistical models have been applied to detect toxic posts, there are only a few studies that focus on detecting the words or expressions that make a post offensive. This motivates the organization of the SemEval-2021 Task 5: Toxic Spans Detection competition, which has provided participants with a dataset containing toxic spans annotation in English posts. In this paper, we present the WLV-RIT entry for the SemEval-2021 Task 5. Our best performing neural transformer model achieves an 0.68 F1-Score. Furthermore, we develop an open-source framework for multilingual detection of offensive spans, i.e., MUDES, based on neural transformers that detect toxic spans in texts

    Consequences of bi-literacy in bilingual individuals: in the healthy and neurologically impaired

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    Background. In the current global, cross-cultural scenario, being bilingual or multilingual is a norm rather than an exception. In such an environment an individual may be actively involved in reading and writing in all their languages in addition to speaking them. Regular use of two or more languages is termed as bilingualism and being able to read and write in both of them is referred to as bi-literacy. Research indicates that bilingualism has an impact on language production and cognition, specifically executive functions. Given the impact of literacy and bilingualism, the reasonable question that arises, is whether bi-literacy would offer an additional impact on language production and cognition. This becomes even more relevant in a multilingual, multi-cultural society such as India. We examined the impact of bi-literacy on oral language production (at word and connected speech level), comprehension and on non-verbal executive function measures in bi-literate bilingual healthy adults in an immigrant diaspora living in the UK. In addition to English, they were speakers of one of the South Indian languages (Kannada, Malayalam, Tamil and Telugu). The significance of bi-literacy among bilinguals assumes further importance in aphasia (language impairment due to brain damage). For those who have aphasia in one or more languages due to brain damage, the severity of impairment maybe different in both languages, also the modalities of language may be differentially affected. In particular, reading and writing maybe impaired differently in the languages used by a bi/multilingual. Manifestation of reading impairments are also dependent on the nature of the script of the language being read [e.g., Raman & Weekes (2005) report differential dyslexia in a Turkish-English speaker who exhibited surface dyslexia in English and deep dysgraphia in Turkish]. Our study contributes to the field of bilingual aphasia by focusing specifically on reading differing from the existing literature of aphasia in bilinguals, where the focus has predominantly been on language production and comprehension. Studying reading impairments provides a better understanding of how the reading impairments are manifested in the two languages, which will aid appropriate assessment and intervention. This research investigated the impact of bi-literacy in both populations (healthy adults and neurologically impaired) in two phases: Phase I (in UK) and Phase II (in India). Aim. Phase I investigated the impact of bi-literacy on oral language production (at word level and connected speech), comprehension and non-verbal executive function in bi-literate bilingual healthy adults. Phase II examined the reading impairments in two languages of bilingual persons with aphasia (BPWA). Methods. For Phase I, participants were thirty-four bi-literate bilingual healthy adults with English as their L2 and one of the Dravidian languages (Kannada, Malayalam, Tamil and Telugu) as their L1. We have used the term ‘print exposure’ as a proxy for literacy. They were divided into a high print exposure (HPE, n=22) and a low print exposure (LPE, n=12) group based on their performance on two tasks measuring L2 print exposure- grammaticality judgement task and sentence verification task. We also quantified their bilingual characteristics- proficiency, reading and writing characteristics and dominance. The groups were matched on years of education, age and gender. Participants completed a set of oral language production tasks in L2 (at word level) namely -verbal fluency, word and non-word repetition; comprehension tasks in L2 namely synonymy triplets task and sentence comprehension task (Chapter 2); oral narrative task in L2 (at connected speech level) (Chapter 3) followed by non-verbal executive function tasks tapping into inhibitory control (Spatial Stroop and Flanker tasks), working memory (visual n-back and auditory n-back) and task switching (colour-shape task) (Chapter 4). For Phase II, we characterized the reading abilities of four BPWA who spoke one of the Dravidian languages (Kannada, Tamil, Telugu) (alpha-syllabic) as their L1 and English (alphabetic) as their L2. We quantified their bilingual characteristics- proficiency, reading and writing characteristics and dominance. Subtests from the Psycholinguistic Assessment of Language Processing in Aphasia (PALPA; Kay, Lesser & Coltheart, 1992) were used to document the reading profile of BPWA in English and reading subtests from Reading Acquisition Profile (RAP-K; Rao, 1997) and words from Bilingual Aphasia test -Hindi (BAT; Paradis & Libben, 1987) were used to document the reading profile of BPWA in Kannada and Hindi respectively. Findings. Based on the findings of Phase I (i.e., results from Chapter 2-4), we found prominent differences between HPE and LPE on comprehension measures (synonymy triplets and sentence comprehension tasks). This is in contrast to the results observed in monolingual adults, were semantics is less impacted by print exposure. Moreover, our predictions that HPE will result in better oral language production skills were borne out in specific conditions-semantic fluency and non-word repetition task (at word level) and higher number of words in the narrative, higher verbs per utterance and fewer repetitions (at connected speech level). In addition, the non-verbal executive functions, we found no direct link between print exposure (in L2) and non-verbal executive functions in bi-literate bilinguals excepting working memory (auditory N-back task). Additionally, another consistency in our findings is that there seems to be a strong link between print exposure and semantic processing in our research. The findings on the semantic tasks have been consistent across comprehension (synonymy triplets task and sentence comprehension task) and production (semantic fluency) favouring HPE. The findings from Phase II (Chapter 5) reveal differences of reading characteristics in the two languages (with different scripts) of the four BPWA. This research provides preliminary evidence that a script related difference exists in the manifestation of dyslexia in bi-scriptal BPWA speaking a combination of alphabetic and alpha-syllabic languages. Conclusions. Our research contributes to the existing literature by highlighting the relationship between bi-literacy and language production, comprehension and non-verbal cognition where bi-literacy seems to have a higher impact on language than cognition. The contrary findings from the monolinguals and children literature, highlight the importance for considering nuances of bilingual research and specifically challenges the notion that semantic comprehension is not significantly affected by literacy. In the neurologically impaired population, our research provides a comprehensive profiling of reading abilities in BPWA in the Indian population with languages having different scripts. Using this profiling and classification, we are able to affirm the findings previously found in literature emphasizing the importance of script in the assessment of reading abilities in BPWA. Such profiling and classification assist in the development of bilingual models of reading aloud and classifying different types of reading impairments

    IndicTrans2: Towards High-Quality and Accessible Machine Translation Models for all 22 Scheduled Indian Languages

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    India has a rich linguistic landscape with languages from 4 major language families spoken by over a billion people. 22 of these languages are listed in the Constitution of India (referred to as scheduled languages) are the focus of this work. Given the linguistic diversity, high-quality and accessible Machine Translation (MT) systems are essential in a country like India. Prior to this work, there was (i) no parallel training data spanning all the 22 languages, (ii) no robust benchmarks covering all these languages and containing content relevant to India, and (iii) no existing translation models which support all the 22 scheduled languages of India. In this work, we aim to address this gap by focusing on the missing pieces required for enabling wide, easy, and open access to good machine translation systems for all 22 scheduled Indian languages. We identify four key areas of improvement: curating and creating larger training datasets, creating diverse and high-quality benchmarks, training multilingual models, and releasing models with open access. Our first contribution is the release of the Bharat Parallel Corpus Collection (BPCC), the largest publicly available parallel corpora for Indic languages. BPCC contains a total of 230M bitext pairs, of which a total of 126M were newly added, including 644K manually translated sentence pairs created as part of this work. Our second contribution is the release of the first n-way parallel benchmark covering all 22 Indian languages, featuring diverse domains, Indian-origin content, and source-original test sets. Next, we present IndicTrans2, the first model to support all 22 languages, surpassing existing models on multiple existing and new benchmarks created as a part of this work. Lastly, to promote accessibility and collaboration, we release our models and associated data with permissive licenses at https://github.com/ai4bharat/IndicTrans2
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