@inproceedings{suryawanshi-etal-2020-nuig,
title = "{NUIG} at {S}em{E}val-2020 Task 12: Pseudo Labelling for Offensive Content Classification",
author = "Suryawanshi, Shardul and
Arcan, Mihael and
Buitelaar, Paul",
editor = "Herbelot, Aurelie and
Zhu, Xiaodan and
Palmer, Alexis and
Schneider, Nathan and
May, Jonathan and
Shutova, Ekaterina",
booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
month = dec,
year = "2020",
address = "Barcelona (online)",
publisher = "International Committee for Computational Linguistics",
url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.semeval-1.208/",
doi = "10.18653/v1/2020.semeval-1.208",
pages = "1598--1604",
abstract = "This work addresses the classification problem defined by sub-task A (English only) of the OffensEval 2020 challenge. We used a semi-supervised approach to classify given tweets into an offensive (OFF) or not-offensive (NOT) class. As the OffensEval 2020 dataset is loosely labelled with confidence scores given by unsupervised models, we used last year`s offensive language identification dataset (OLID) to label the OffensEval 2020 dataset. Our approach uses a pseudo-labelling method to annotate the current dataset. We trained four text classifiers on the OLID dataset and the classifier with the highest macro-averaged F1-score has been used to pseudo label the OffensEval 2020 dataset. The same model which performed best amongst four text classifiers on OLID dataset has been trained on the combined dataset of OLID and pseudo labelled OffensEval 2020. We evaluated the classifiers with precision, recall and macro-averaged F1-score as the primary evaluation metric on the OLID and OffensEval 2020 datasets. This work is licensed under a Creative Commons Attribution 4.0 International Licence. Licence details: \url{http://creativecommons.org/licenses/by/4.0/}."
}
Markdown (Informal)
[NUIG at SemEval-2020 Task 12: Pseudo Labelling for Offensive Content Classification](https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.semeval-1.208/) (Suryawanshi et al., SemEval 2020)
ACL