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

arXiv: https://arxiv.org/abs/2103.00464 · HTML: https://mlsanity.com/paper/2103.00464/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 25 references that are in the map.
- First posted on arXiv 28 Feb 2021.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Learning Word Vectors for 157 Languages](https://mlsanity.com/paper/1802.06893/) (2018)
- [ktrain: A Low-Code Library for Augmented Machine Learning](https://mlsanity.com/paper/2004.10703/) (2020)
- [Experimental Evaluation of Deep Learning models for Marathi Text Classification](https://mlsanity.com/paper/2101.04899/) (2021)
- [Transformer-based Language Model Fine-tuning Methods for COVID-19 Fake News Detection](https://mlsanity.com/paper/2101.05509/) (2021)
- [Voice for the Voiceless: Active Sampling to Detect Comments Supporting the Rohingyas](https://mlsanity.com/paper/1910.03206/) (2019)
- [Combating Hostility: Covid-19 Fake News and Hostile Post Detection in Social Media](https://mlsanity.com/paper/2101.03291/) (2021)
- [TechTexC: Classification of Technical Texts using Convolution and Bidirectional Long Short Term Memory Network](https://mlsanity.com/paper/2012.11420/) (2020)

## Built on by (most cited first, 7 of 7)

- [The Decades Progress on Code-Switching Research in NLP: A Systematic Survey on Trends and Challenges](https://mlsanity.com/paper/2212.09660/) (2022)
- [PolyHope: Two-Level Hope Speech Detection from Tweets](https://mlsanity.com/paper/2210.14136/) (2022)
- [Hope Speech detection in under-resourced Kannada language](https://mlsanity.com/paper/2108.04616/) (2021)
- [Beyond Monolingual Assumptions: A Survey of Code-Switched NLP in the Era of Large Language Models across Modalities](https://mlsanity.com/paper/2510.07037/) (2025)
- [Code-mixed Sentiment and Hate-speech Prediction](https://mlsanity.com/paper/2405.12929/) (2024)
- [Beyond Negativity: Re-Analysis and Follow-Up Experiments on Hope Speech Detection](https://mlsanity.com/paper/2306.01742/) (2023)
- [cantnlp@DravidianLangTech 2026: organic domain adaptation improves multi-class hope speech detection in Tulu](https://mlsanity.com/paper/2605.09795/) (2026)

## Abstract (arXiv)

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.