@inproceedings{dsa-etal-2020-label,
title = "Label Propagation-Based Semi-Supervised Learning for Hate Speech Classification",
author = "D{'}Sa, Ashwin Geet and
Illina, Irina and
Fohr, Dominique and
Klakow, Dietrich and
Ruiter, Dana",
editor = "Rogers, Anna and
Sedoc, Jo{\~a}o and
Rumshisky, Anna",
booktitle = "Proceedings of the First Workshop on Insights from Negative Results in NLP",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.insights-1.8",
doi = "10.18653/v1/2020.insights-1.8",
pages = "54--59",
abstract = "Research on hate speech classification has received increased attention. In real-life scenarios, a small amount of labeled hate speech data is available to train a reliable classifier. Semi-supervised learning takes advantage of a small amount of labeled data and a large amount of unlabeled data. In this paper, label propagation-based semi-supervised learning is explored for the task of hate speech classification. The quality of labeling the unlabeled set depends on the input representations. In this work, we show that pre-trained representations are label agnostic, and when used with label propagation yield poor results. Neural network-based fine-tuning can be adopted to learn task-specific representations using a small amount of labeled data. We show that fully fine-tuned representations may not always be the best representations for the label propagation and intermediate representations may perform better in a semi-supervised setup.",
}
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<abstract>Research on hate speech classification has received increased attention. In real-life scenarios, a small amount of labeled hate speech data is available to train a reliable classifier. Semi-supervised learning takes advantage of a small amount of labeled data and a large amount of unlabeled data. In this paper, label propagation-based semi-supervised learning is explored for the task of hate speech classification. The quality of labeling the unlabeled set depends on the input representations. In this work, we show that pre-trained representations are label agnostic, and when used with label propagation yield poor results. Neural network-based fine-tuning can be adopted to learn task-specific representations using a small amount of labeled data. We show that fully fine-tuned representations may not always be the best representations for the label propagation and intermediate representations may perform better in a semi-supervised setup.</abstract>
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%0 Conference Proceedings
%T Label Propagation-Based Semi-Supervised Learning for Hate Speech Classification
%A D’Sa, Ashwin Geet
%A Illina, Irina
%A Fohr, Dominique
%A Klakow, Dietrich
%A Ruiter, Dana
%Y Rogers, Anna
%Y Sedoc, João
%Y Rumshisky, Anna
%S Proceedings of the First Workshop on Insights from Negative Results in NLP
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F dsa-etal-2020-label
%X Research on hate speech classification has received increased attention. In real-life scenarios, a small amount of labeled hate speech data is available to train a reliable classifier. Semi-supervised learning takes advantage of a small amount of labeled data and a large amount of unlabeled data. In this paper, label propagation-based semi-supervised learning is explored for the task of hate speech classification. The quality of labeling the unlabeled set depends on the input representations. In this work, we show that pre-trained representations are label agnostic, and when used with label propagation yield poor results. Neural network-based fine-tuning can be adopted to learn task-specific representations using a small amount of labeled data. We show that fully fine-tuned representations may not always be the best representations for the label propagation and intermediate representations may perform better in a semi-supervised setup.
%R 10.18653/v1/2020.insights-1.8
%U https://aclanthology.org/2020.insights-1.8
%U https://doi.org/10.18653/v1/2020.insights-1.8
%P 54-59
Markdown (Informal)
[Label Propagation-Based Semi-Supervised Learning for Hate Speech Classification](https://aclanthology.org/2020.insights-1.8) (D’Sa et al., insights 2020)
ACL