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Found 9 papers, 3 papers with code
Exploring Data Augmentation Strategies for Hate Speech Detection in Roman Urdu
In this paper, we explore different data augmentation techniques for the improvement of hate speech detection in Roman Urdu.
Hate-Speech and Offensive Language Detection in Roman Urdu
The task of automatic hate-speech and offensive language detection in social media content is of utmost importance due to its implications in unprejudiced society concerning race, gender, or religion.
A Clustering Framework for Lexical Normalization of Roman Urdu
Roman Urdu is an informal form of the Urdu language written in Roman script, which is widely used in South Asia for online textual content.
Adapting Deep Learning for Sentiment Classification of Code-Switched Informal Short Text
Such informal and code-switched content are under-resourced in terms of labeled datasets and language models even for popular tasks like sentiment classification.
A Multi-cascaded Model with Data Augmentation for Enhanced Paraphrase Detection in Short Texts
In this work, we present a data augmentation strategy and a multi-cascaded model for improved paraphrase detection in short texts.
A Multi-cascaded Deep Model for Bilingual SMS Classification
Our model achieves high accuracy for classification on this dataset and outperforms the previous model for multilingual text classification, highlighting language independence of McM.
Improving Text Normalization by Optimizing Nearest Neighbor Matching
This approach is motivated by the observation that text normalization is essentially a matching problem and nearest neighbor matching with an adaptive similarity function is the most direct procedure for it.
