AdamR at SemEval-2023 Task 10: Solving the Class Imbalance Problem in Sexism Detection with Ensemble Learning

2023-05-15 · Adam Rydelek, Daryna Dementieva, Georg Groh

The Explainable Detection of Online Sexism task presents the problem of explainable sexism detection through fine-grained categorisation of sexist cases with three subtasks. Our team experimented with different ways to combat class imbalance throughout the tasks using data augmentation and loss alteration techniques. We tackled the challenge by utilising ensembles of Transformer models trained on different datasets, which are tested to find the balance between performance and interpretability. This solution ranked us in the top 40\% of teams for each of the tracks.

📄 PDF Abstract BibTeX arXiv:2305.08636

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Tasks

Data Augmentation Ensemble Learning

Methods 이 논문이 사용한 방법론

Attention 설명 없음

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

Position-Wise Feed-Forward Layer 설명 없음

Residual Connection 설명 없음

Multi-Head Attention 설명 없음

Adam 설명 없음

Absolute Position Encodings Absolute Position Encodings are a type of position embeddings for [Transformer-based models] where positional encodings are…

Softmax The Softmax output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$…

AdaMR: Adaptable Molecular Representation for Unified Pre-training Strategy

2023-12-28 · Yan Ding, Hao Cheng, Ziliang Ye, Ruyi Feng 외

We propose Adjustable Molecular Representation (AdaMR), a new large-scale uniform pre-training strategy for small-molecule drugs, as a novel unified pre-training strategy. AdaMR utilizes a granularity-adjustable molecula…

Attribute Molecular Property Prediction molecular representation Property Prediction

HadamRNN: Binary and Sparse Ternary Orthogonal RNNs

2025-01-28 · Armand Foucault, Franck Mamalet, François Malgouyres

Binary and sparse ternary weights in neural networks enable faster computations and lighter representations, facilitating their use on edge devices with limited computational power. Meanwhile, vanilla RNNs are highly sen…

Binarization

QUB at SemEval-2017 Task 6: Cascaded Imbalanced Classification for Humor Analysis in Twitter

2017-08-01 · SEMEVAL 2017 8 · Xiwu Han, Gregory Toner

This paper presents our submission to SemEval-2017 Task 6: {\#}HashtagWars: Learning a Sense of Humor. There are two subtasks: A. Pairwise Comparison, and B. Semi-Ranking. Our assumption is that the distribution of humor…

General Classification Humor Detection imbalanced classification

CN-HIT-MI.T at SemEval-2020 Task 8: Memotion Analysis Based on BERT

2020-12-01 · SEMEVAL 2020 · Zhen Li, Yaojie Zhang, Bing Xu, Tiejun Zhao

Internet memes emotion recognition is focused by many researchers. In this paper, we adopt BERT and ResNet for evaluation of detecting the emotions of Internet memes. We focus on solving the problem of data imbalance and…

Emotion Recognition

JCT at SemEval-2022 Task 4-A: Patronism Detection in Posts Written in English using Preprocessing Methods and various Machine Leaerning Methods

2022-07-01 · SemEval (NAACL) 2022 7 · Yaakov HaCohen-Kerner, Ilan Meyrowitsch, Matan Fchima

In this paper, we describe our submissions to SemEval-2022 subtask 4-A - “Patronizing and Condescending Language Detection: Binary Classification”. We developed different models for this subtask. We applied 11 supervised…

Binary Classification