NLP-CUET@LT-EDI-EACL2021: Multilingual Code-Mixed Hope Speech Detection using Cross-lingual Representation Learner

TL;DR

This paper presents multilingual hope speech detection models for English, Tamil, and Malayalam, demonstrating that transformer-based approaches like XLM-Roberta outperform other methods with high accuracy, and achieved top ranks in the shared tasks.

Contribution

The work introduces a multilingual hope speech detection framework using transformer models, achieving state-of-the-art results across three languages and establishing new benchmarks.

Findings

XLM-Roberta achieved the highest weighted F1-scores: 0.93 (English), 0.60 (Tamil), 0.85 (Malayalam).

Transformer-based models outperform traditional machine learning and deep learning methods.

The team secured top ranks in all three language tasks.

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 (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 $f_1$-score of $0.93$, $0.60$ and $0.85$ respectively for English, Tamil and Malayalam language. Our team has achieved $1^{st}$, $2^{nd}$ and $1^{st}$ rank in these three tasks respectively.