BRUMS at HASOC 2019: Deep learning models for multilingual hate speech and offensive language identification

Tharindu Ranasinghe, Marcos Zampieri, Hansi Hettiarachchi

    Research output: Chapter in Book/Published conference outputConference publication

    Abstract

    In this paper, we describe the BRUMS entry to the Hate Speech and Offensive Content Identification in Indo-European Languages
    (HASOC) shared task 2019. The HASOC organizers provided participants with annotated datasets containing posts from social media in
    English, German, and Hindi (including code-mixing). We present a multilingual deep learning model to identify hate speech and offensive language in social media. Our best performing system was ranked 3rd among 79 entries in the English track of the HASOC sub-task 1.
    Original languageEnglish
    Title of host publicationWorking Notes of FIRE 2019 - Forum for Information Retrieval Evaluation
    PublisherCEUR-WS.org
    Pages199-207
    Publication statusPublished - Dec 2019

    Publication series

    NameCEUR Workshop Proceedings
    PublisherCEUR-WS.org
    Volume2517
    ISSN (Electronic)1613-0073

    Fingerprint

    Dive into the research topics of 'BRUMS at HASOC 2019: Deep learning models for multilingual hate speech and offensive language identification'. Together they form a unique fingerprint.

    Cite this