Papers › NLP-CUET@LT-EDI-EACL2021: Multilingual Code-Mixed Hope Speech Detection using...
NLP-CUET@LT-EDI-EACL2021: Multilingual Code-Mixed Hope Speech Detection using Cross-lingual Representation Learner
Eftekhar Hossain, Omar Sharif, Mohammed Moshiul Hoque
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 (support vector machine, logistic regression, ensemble), deep learning (convolutional neural network + long short term memory) and transformer (m-BERT, Indic-BERT, XLNet, XLM-Roberta) based methods. Results indicate that XLM-Roberta outdoes all other techniques by gaining a weighted f₁-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.
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