Ceasing hate with MoH: Hate Speech Detection in Hindi–English code-switched language
| Autor: | Minni Jain, Arushi Sharma, Anubha Kabra |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
Hindi
Voice activity detection Language identification Computer science business.industry media_common.quotation_subject Library and Information Sciences Management Science and Operations Research computer.software_genre language.human_language Code (semiotics) Computer Science Applications Hatred Devanagari Media Technology language Transliteration Language model Artificial intelligence business computer Natural language processing Information Systems media_common |
| Zdroj: | Information Processing & Management. 59:102760 |
| ISSN: | 0306-4573 |
| Popis: | Warning: This manuscript may contain upsetting language. Social media has become a bedrock for people to voice their opinions worldwide. Due to the greater sense of freedom with the anonymity feature, it is possible to disregard social etiquette online and attack others without facing severe consequences, inevitably propagating hate speech. The current measures to sift the online content and offset the hatred spread do not go far enough. One factor contributing to this is the prevalence of regional languages in social media and the paucity of language flexible hate speech detectors. The proposed work focuses on analyzing hate speech in Hindi–English code-switched language. Our method explores transformation techniques to capture precise text representation. To contain the structure of data and yet use it with existing algorithms, we developed ‘MoH’ or (Map Only Hindi), which means ‘Love’ in Hindi. ‘MoH’ pipeline which consists of language identification, Roman to Devanagari Hindi transliteration using a knowledge base of Roman Hindi words, and finally employs the fine-tuned Multilingual Bert, and MuRIL language models. We conducted several quantitative experiment studies on three datasets, and evaluated performance using Precision, Recall and F1 metrics. The first experiment studies ‘MoH’ mapped text’s performance with classical machine learning models and shows an average increase of 13% in F1 scores. The second compares the proposed work’s scores with those of the baseline models and shows a rise in performance by 6%. Finally, the third compares the proposed ‘MoH’ technique with various data simulations using the existing transliteration library. Here, ‘MoH’ outperforms the rest by 15%. Our results demonstrate a significant improvement in the state-of-the-art scores on all three datasets. |
| Databáze: | OpenAIRE |
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