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indicnlp@kgp at DravidianLangTech-EACL2021: Offensive Language Identification in Dravidian Languages

2021-02-14 · EACL (DravidianLangTech) 2021 4 · Kushal Kedia, Abhilash Nandy

The paper presents the submission of the team indicnlp@kgp to the EACL 2021 shared task "Offensive Language Identification in Dravidian Languages." The task aimed to classify different offensive content types in 3 code-mixed Dravidian language datasets. The work leverages existing state of the art approaches in text classification by incorporating additional data and transfer learning on pre-trained models. Our final submission is an ensemble of an AWD-LSTM based model along with 2 different transformer model architectures based on BERT and RoBERTa. We achieved weighted-average F1 scores of 0.97, 0.77, and 0.72 in the Malayalam-English, Tamil-English, and Kannada-English datasets ranking 1st, 2nd, and 3rd on the respective tasks.

📄 PDF Abstract BibTeX arXiv:2102.07150

Code (1)

kushal2000/Dravidian-Offensive-Language-Identification 공식 구현

Tasks

Language Identificationtext-classificationText ClassificationTransfer Learning

Methods 이 논문이 사용한 방법론

Linear Layer A Linear Layer is a projection $\mathbf{XW + b}$.
Residual Connection 설명 없음
Weight Decay 설명 없음
Multi-Head Attention 설명 없음
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Attention Dropout Attention Dropout is a type of dropout used in attention-based architectures, where elements are randomly dropped out of the…
Variational Dropout Variational Dropout is a regularization technique based on dropout, but uses a variational inference grounded approach. In…
Activation Regularization Activation Regularization (AR), or $L\_{2}$ activation regularization, is regularization performed on activations as opposed to weights. It is usually used in conjunction with…

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