Fighting the COVID-19 Infodemic with a Holistic BERT Ensemble

2021-04-12 · NAACL (NLP4IF) 2021 6 · Giorgos Tziafas, Konstantinos Kogkalidis, Tommaso Caselli

This paper describes the TOKOFOU system, an ensemble model for misinformation detection tasks based on six different transformer-based pre-trained encoders, implemented in the context of the COVID-19 Infodemic Shared Task for English. We fine tune each model on each of the task's questions and aggregate their prediction scores using a majority voting approach. TOKOFOU obtains an overall F1 score of 89.7%, ranking first.

📄 PDF Abstract BibTeX arXiv:2104.05745

Code (1)

gtziafas/nlp4ifchallenge 공식 구현 pytorch

Tasks

Misinformation

Methods 이 논문이 사용한 방법론

Linear Layer A Linear Layer is a projection $\mathbf{XW + b}$.

Residual Connection 설명 없음

Weight Decay 설명 없음

Attention Dropout Attention Dropout is a type of dropout used in attention-based architectures, where elements are randomly dropped out of the…

Linear Warmup With Linear Decay Linear Warmup With Linear Decay is a learning rate schedule in which we increase the learning rate linearly for $n$ updates and then linearly decay afterwards.

WordPiece 설명 없음

Adam 설명 없음

Dropout Dropout is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is…

R00 at NLP4IF-2021 Fighting COVID-19 Infodemic with Transformers and More Transformers

2021-06-01 · NAACL (NLP4IF) 2021 6 · Ahmed Qarqaz, Dia Abujaber, Malak Abdullah

This paper describes the winning model in the Arabic NLP4IF shared task for fighting the COVID-19 infodemic. The goal of the shared task is to check disinformation about COVID-19 in Arabic tweets. Our proposed model has …

iCompass at NLP4IF-2021–Fighting the COVID-19 Infodemic

2021-06-01 · Wassim Henia, Oumayma Rjab, Hatem Haddad, Chayma Fourati

This paper provides a detailed overview of the system and its outcomes, which were produced as part of the NLP4IF Shared Task on Fighting the COVID-19 Infodemic at NAACL 2021. This task is accomplished using a variety of…

Multi Output Learning using Task Wise Attention for Predicting Binary Properties of Tweets : Shared-Task-On-Fighting the COVID-19 Infodemic

2021-06-01 · NAACL (NLP4IF) 2021 6 · Ayush Suhane, Shreyas Kowshik

In this paper, we describe our system for the shared task on Fighting the COVID-19 Infodemic in the English Language. Our proposed architecture consists of a multi-output classification model for the seven tasks, with a …

Position

Fighting the COVID-19 Infodemic in Social Media: A Holistic Perspective and a Call to Arms

2020-07-15 · Firoj Alam, Fahim Dalvi, Shaden Shaar, Nadir Durrani 외

With the outbreak of the COVID-19 pandemic, people turned to social media to read and to share timely information including statistics, warnings, advice, and inspirational stories. Unfortunately, alongside all this usefu…

Misinformation

DamascusTeam at NLP4IF2021: Fighting the Arabic COVID-19 Infodemic on Twitter Using AraBERT

2021-06-01 · NAACL (NLP4IF) 2021 6 · Ahmad Hussein, Nada Ghneim, Ammar Joukhadar

The objective of this work was the introduction of an effective approach based on the AraBERT language model for fighting Tweets COVID-19 Infodemic. It was arranged in the form of a two-step pipeline, where the first ste…

Language Modeling Language Modelling