IUST at ClimateActivism 2024: Towards Optimal Stance Detection: A Systematic Study of Architectural Choices and Data Cleaning Techniques

2024-03-22 · CASE at EACL 2024 3 · Ghazaleh Mahmoudi, Sauleh Eetemadi

This work presents a systematic search of various model architecture configurations and data cleaning methods. The study evaluates the impact of data cleaning methods on the obtained results. Additionally, we demonstrate that a combination of CNN and Encoder-only models such as BERTweet outperforms FNNs. Moreover, by utilizing data augmentation, we are able to overcome the challenge of data imbalance.

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Code (1)

ghazaleh-mahmoodi/Climate_Activism_Stance_Detection 공식 구현 pytorch

Tasks

Data Augmentation Stance Detection

Methods 이 논문이 사용한 방법론

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

Residual Connection 설명 없음

Weight Decay 설명 없음

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Bryndza at ClimateActivism 2024: Stance, Target and Hate Event Detection via Retrieval-Augmented GPT-4 and LLaMA

2024-02-09 · Marek Šuppa, Daniel Skala, Daniela Jašš, Samuel Sučík 외

This study details our approach for the CASE 2024 Shared Task on Climate Activism Stance and Hate Event Detection, focusing on Hate Speech Detection, Hate Speech Target Identification, and Stance Detection as classificat…

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IUST_PersonReId: A New Domain in Person Re-Identification Datasets

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The task of identifying public opinions on social media, particularly regarding climate activism and the detection of hate events, has emerged as a critical area of research in our rapidly changing world. With a growing …

Data Augmentation

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