Exploiting Unsupervised Pre-training and Automated Feature Engineering for Low-resource Hate Speech Detection in Polish

2019-06-17 · Renard Korzeniowski, Rafał Rolczyński, Przemysław Sadownik, Tomasz Korbak, Marcin Możejko

This paper presents our contribution to PolEval 2019 Task 6: Hate speech and bullying detection. We describe three parallel approaches that we followed: fine-tuning a pre-trained ULMFiT model to our classification task, fine-tuning a pre-trained BERT model to our classification task, and using the TPOT library to find the optimal pipeline. We present results achieved by these three tools and review their advantages and disadvantages in terms of user experience. Our team placed second in subtask 2 with a shallow model found by TPOT: a~logistic regression classifier with non-trivial feature engineering.

📄 PDF Abstract BibTeX arXiv:1906.09325

Code (0)

등록된 구현이 없습니다.

Tasks

Automated Feature EngineeringClassification Feature Engineering General Classification Hate Speech Detection regression Unsupervised Pre-training

Methods 이 논문이 사용한 방법론

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

Sigmoid Activation 설명 없음

Tanh Activation 설명 없음

Residual Connection 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.

Weight Decay 설명 없음

Refunds@Expedia|||How do I get a full refund from Expedia? “How do I get a full refund from Expedia? How do I get a full refund from Expedia? – Call ☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056 for Quick Help &…

Automated Essay Scoring with Discourse-Aware Neural Models

2019-08-01 · WS 2019 8 · Farah Nadeem, Huy Nguyen, Yang Liu, Mari Ostendorf

Automated essay scoring systems typically rely on hand-crafted features to predict essay quality, but such systems are limited by the cost of feature engineering. Neural networks offer an alternative to feature engineeri…

Automated Essay Scoring Feature Engineering

Learned Feature Importance Scores for Automated Feature Engineering

2024-06-06 · Yihe Dong, Sercan Arik, Nathanael Yoder, Tomas Pfister

Feature engineering has demonstrated substantial utility for many machine learning workflows, such as in the small data regime or when distribution shifts are severe. Thus automating this capability can relieve much manu…

Automated Feature Engineering Feature Engineering Feature Importance Time Series

Weighted Unsupervised Domain Adaptation Considering Geometry Features and Engineering Performance of 3D Design Data

2023-09-08 · Seungyeon Shin, Namwoo Kang

The product design process in manufacturing involves iterative design modeling and analysis to achieve the target engineering performance, but such an iterative process is time consuming and computationally expensive. Re…

Domain Adaptation Unsupervised Domain Adaptation

Automating Feature Engineering

2016-01-01 · NIPS 2016 2016 1 · Udayan Khurana, Fatemeh Nargesian, Horst Samulowitz, Elias Khalil 외

Feature Engineering is the task of transforming the feature space in a given learning problem to improve the performance of a trained model. It is a crucial but time intensive and skillful process, involving a data scien…

Automated Feature Engineering Feature Engineering

Learning a Data-Driven Policy Network for Pre-Training Automated Feature Engineering

2023-02-02 · Conference 2023 2 · Liyao Li, Haobo Wang, Liangyu Zha, Qingyi Huang 외

Feature engineering is widely acknowledged to be pivotal in tabular data analysis and prediction. Automated feature engineering (AutoFE) emerged to automate this process managed by experienced data scientists and enginee…

Automated Feature Engineering Feature Engineering Neural Architecture Search