NLP-CUET@LT-EDI-EACL2021: Multilingual Code-Mixed Hope Speech Detection using Cross-lingual Representation Learner
Published in
(2021)External Links:
TL
TL;DR
This work proposes three distinct models to identify hope speech in English, Tamil and Malayalam language to serve this purpose and indicates that XLM-R outdoes all other techniques by gaining a weighted f_1-score.
Abstract
In recent years, several systems have been developed to regulate the spread of negativity and eliminate aggressive, offensive or abusive contents from the online platforms. Nevertheless, a limited number of researches carried out to identify positive, encouraging and supportive contents. In this work, our goal is to identify whether a social media post/comment contains hope speech or not. We propose three distinct models to identify hope speech in English, Tamil and Malayalam language to serve this purpose. To attain this goal, we employed various machine learning (SVM, LR, ensemble), deep learning (CNN+BiLSTM) and transformer (m-BERT, Indic-BERT, XLNet, XLM-R) based methods. Results indicate that XLM-R outdoes all other techniques by gaining a weighted f_1-score of 0.93, 0.60 and 0.85 respectively for English, Tamil and Malayalam language. Our team has achieved 1st, 2nd and 1st rank in these three tasks respectively.
References25 items
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Overview of the HASOC track at FIRE 2020: Hate Speech and Offensive Content Identification in Indo-European Languages
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Findings of the Shared Task on Hope Speech Detection for Equality, Diversity, and Inclusion
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