Meta-Learning for Offensive Language Detection in Code-Mixed Texts

Suresh, Gautham Vadakkekara; Chakravarthi, Bharathi Raja; McCrae, John P.

doi:10.1145/3503162.3503167

Cited by 7 publications

(3 citation statements)

References 23 publications

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“…Compared to the more general NLP tasks, there are much fewer works focusing on the use of meta-learning in hate speech detection, especially in the cross-lingual setting. Vadakkekara Suresh, Chakravarthi & McCrae (2022) proposed a two-step strategy using meta-learning algorithms to identify offensive text in Tamil-English and Malayalam-English code-mixed texts. The authors introduced a weighted data sampling approach to enable better convergence in the meta-training phase compared to conventional methods.…”

Section: Approaches On Multilingual Hate Speech Detectionmentioning

confidence: 99%

A survey on multi-lingual offensive language detection

Mnassri,

Farahbakhsh,

Chalehchaleh

et al. 2024

PeerJ Computer Science

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The prevalence of offensive content on online communication and social media platforms is growing more and more common, which makes its detection difficult, especially in multilingual settings. The term “Offensive Language” encompasses a wide range of expressions, including various forms of hate speech and aggressive content. Therefore, exploring multilingual offensive content, that goes beyond a single language, focus and represents more linguistic diversities and cultural factors. By exploring multilingual offensive content, we can broaden our understanding and effectively combat the widespread global impact of offensive language. This survey examines the existing state of multilingual offensive language detection, including a comprehensive analysis on previous multilingual approaches, and existing datasets, as well as provides resources in the field. We also explore the related community challenges on this task, which include technical, cultural, and linguistic ones, as well as their limitations. Furthermore, in this survey we propose several potential future directions toward more efficient solutions for multilingual offensive language detection, enabling safer digital communication environment worldwide.

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Section: Approaches On Multilingual Hate Speech Detectionmentioning

confidence: 99%

A survey on multi-lingual offensive language detection

Mnassri,

Farahbakhsh,

Chalehchaleh

et al. 2024

PeerJ Computer Science

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“…X-MAML explores various auxiliary languages to identify the optimal composition for zero-shot cross-lingual transfer. Meta-learning has also been applied in the detection of offensive language in cross-lingual and code-mixed texts [40], [41] and other harmful content such as multilingual rumours [42]. However, the limited availability of multilingual hate speech datasets, comprising of only two or three languages, presents a challenge in finding an effective auxiliary language.…”

Section: Related Work Multilingual Hate Speech Detectionmentioning

confidence: 99%

Model-Agnostic Meta-Learning for Multilingual Hate Speech Detection

Awal¹,

Lee²,

Tanwar³

et al. 2023

Preprint

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Hate speech in social media is a growing phenomenon, and detecting such toxic content has recently gained significant traction in the research community. Existing studies have explored fine-tuning language models (LMs) to perform hate speech detection, and these solutions have yielded significant performance. However, most of these studies are limited to detecting hate speech only in English, neglecting the bulk of hateful content that is generated in other languages, particularly in low-resource languages. Developing a classifier that captures hate speech and nuances in a low-resource language with limited data is extremely challenging. To fill the research gap, we propose HateMAML, a model-agnostic meta-learning-based framework that effectively performs hate speech detection in low-resource languages. HateMAML utilizes a self-supervision strategy to overcome the limitation of data scarcity and produces better LM initialization for fast adaptation to an unseen target language (i.e., cross-lingual transfer) or other hate speech datasets (i.e., domain generalization). Extensive experiments are conducted on five datasets across eight different low-resource languages. The results show that HateMAML outperforms the state-of-the-art baselines by more than 3% in the cross-domain multilingual transfer setting. We also conduct ablation studies to analyze the characteristics of HateMAML.

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“…Social media today plays a vital role in spreading hatred and provoking people, which gives rise to hate-related crimes (Vadakkekara Suresh et al, 2021). Various hate-related terror attacks usually have a history of hate-related content in their social media accounts.…”

Section: Introduction and Related Workmentioning

confidence: 99%

IDIAP_TIET@LT-EDI-ACL2022 : Hope Speech Detection in Social Media using Contextualized BERT with Attention Mechanism

Deepanshu¹,

Singh²,

Motlíček³

2022

Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion

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With the increase of users on social media platforms, manipulating or provoking masses of people has become a piece of cake. This spread of hatred among people, which has become a loophole for freedom of speech, must be minimized. Hence, it is essential to have a system that automatically classifies the hatred content, especially on social media, to take it down. This paper presents a simple modular pipeline classifier with BERT embeddings and attention mechanism to classify hope speech content in the Hope Speech Detection shared task for Equality, Diversity, and Inclusion-ACL 2022. Our system submission ranks fourth with an F1-score of 0.84. We release our code-base here https: //github.com/Deepanshu-beep/ hope-speech-attention.

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Meta-Learning for Offensive Language Detection in Code-Mixed Texts

Cited by 7 publications

References 23 publications

A survey on multi-lingual offensive language detection

A survey on multi-lingual offensive language detection

Model-Agnostic Meta-Learning for Multilingual Hate Speech Detection

IDIAP_TIET@LT-EDI-ACL2022 : Hope Speech Detection in Social Media using Contextualized BERT with Attention Mechanism

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