Multilingual Cyber Abuse Detection using Advanced Transformer Architecture

Malte, Aditya; Ratadiya, Pratik

doi:10.1109/tencon.2019.8929493

Cited by 33 publications

(22 citation statements)

References 3 publications

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“…Recent years have seen a rise in the use of transfer learning in language processing (Malte and Ratadiya, 2019a) owing to their superior performance. Models based on underlying concepts like attention mechanism and transformers are seeing widespread use across a range of tasks (Malte and Ratadiya, 2019b;Ratadiya and Mishra, 2019). Our findings concurred with this trend as we used similar kinds of architectures as the fundamental blocks in the systems for both the subtasks.…”

Section: Introductionsupporting

confidence: 85%

AttesTable at SemEval-2021 Task 9: Extending Statement Verification with Tables for Unknown Class, and Semantic Evidence Finding

Varma¹,

Jain²,

Ratadiya³

et al. 2021

Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

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This paper describes our approach for Task 9 of SemEval 2021: Statement Verification and Evidence Finding with Tables. We participated in both subtasks, namely statement verification and evidence finding. For the subtask of statement verification, we extend the TAPAS model to adapt to the 'unknown' class of statements by finetuning it on an augmented version of the task data. For the subtask of evidence finding, we finetune the DistilBERT model in a Siamese setting.

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Section: Introductionsupporting

confidence: 85%

AttesTable at SemEval-2021 Task 9: Extending Statement Verification with Tables for Unknown Class, and Semantic Evidence Finding

Varma¹,

Jain²,

Ratadiya³

et al. 2021

Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

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“…Using a bidirectional transformer‐based BERT Architecture, Aditya Malte and Pratik Ratadiya 86 detected cyber abuse from Facebook multilingual texts, English, Hindi, and a mixture of both languages (Hinglish) texts. The dataset contained 25 013 multilingual comments made up of 12 000, training sets each for English and Hindi comments and a separate 916 English, 970 Hindi comments used for testing plus an additional 1257 English tweets and 1194 Hindi tweets which were used to reinforce the model's generalization ability.…”

Section: Detection Approaches and Related Workmentioning

confidence: 99%

Text‐based emotion detection: Advances, challenges, and opportunities

Acheampong

Chen

Nunoo‐Mensah

2020

Engineering Reports

220

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Emotion detection (ED) is a branch of sentiment analysis that deals with the extraction and analysis of emotions. The evolution of Web 2.0 has put text mining and analysis at the frontiers of organizational success. It helps service providers provide tailor-made services to their customers. Numerous studies are being carried out in the area of text mining and analysis due to the ease in sourcing for data and the vast benefits its deliverable offers. This article surveys the concept of ED from texts and highlights the main approaches adopted by researchers in the design of text-based ED systems. The article further discusses some recent state-of-the-art proposals in the field. The proposals are discussed in relation to their major contributions, approaches employed, datasets used, results obtained, strengths, and their weaknesses. Also, emotion-labeled data sources are presented to provide neophytes with eligible text datasets for ED. Finally, the article presents some open issues and future research direction for text-based ED. K E Y W O R D S emotion detection, natural language processing, sentiment analysis, text-based emotion detection This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…A Malte et al. [25] detected Cyber abuse words in languages Hindi and English texts were analyzed using the BERT model. When compared to machine learning models, the pre-trained transformer based classification models ALBERT, BiLSTM, distilBERT, BERT, XLNet, and RoBERTa demonstrate greater accuracy.…”

Section: Related Workmentioning

confidence: 99%