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BertOdia: BERT Pre-training for Low Resource Odia Language

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3A4PDW2E5E" target="_blank" >RIV/00216208:11320/22:4PDW2E5E - isvavai.cz</a>

  • Result on the web

    <a href="https://doi.org/10.1007/978-981-16-8739-6_32" target="_blank" >https://doi.org/10.1007/978-981-16-8739-6_32</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/978-981-16-8739-6_32" target="_blank" >10.1007/978-981-16-8739-6_32</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    BertOdia: BERT Pre-training for Low Resource Odia Language

  • Original language description

    Odia language is one of the 30 most spoken languages in the world. It is spoken in the Indian state called Odisha. Odia language lacks online content and resources for natural language processing (NLP) research. There is a great need for a better language model for the low resource Odia language, which can be used for many downstream NLP tasks. In this paper, we introduce a Bert-based language model, pre-trained on 430,000 Odia sentences. We also evaluate the model on the well-known Kaggle Odia news classification dataset (BertOdia: 96%, RoBERTaOdia: 92%, and ULMFit: 91.9% classification accuracy), and perform a comparison study with multilingual Bidirectional Encoder Representations from Transformers (BERT) supporting Odia. The model will be released publicly for the researchers to explore other NLP tasks.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

Others

  • Publication year

    2022

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Biologically Inspired Techniques in Many Criteria Decision Making

  • ISBN

    978-981-16-8739-6

  • ISSN

  • e-ISSN

  • Number of pages

    11

  • Pages from-to

    345-355

  • Publisher name

    Springer Nature

  • Place of publication

  • Event location

    Singapore

  • Event date

    Jan 1, 2022

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article