Hitachi at SemEval-2020 Task 12: Offensive Language Identification with Noisy Labels Using Statistical Sampling and Post-Processing

Manikandan Ravikiran, Amin Ekant Muljibhai, Toshinori Miyoshi, Hiroaki Ozaki, Yuta Koreeda, Sakata Masayuki


Abstract
In this paper, we present our participation in SemEval-2020 Task-12 Subtask-A (English Language) which focuses on offensive language identification from noisy labels. To this end, we developed a hybrid system with the BERT classifier trained with tweets selected using Statistical Sampling Algorithm (SA) and Post-Processed (PP) using an offensive wordlist. Our developed system achieved 34th position with Macro-averaged F1-score (Macro-F1) of 0.90913 over both offensive and non-offensive classes. We further show comprehensive results and error analysis to assist future research in offensive language identification with noisy labels.
Anthology ID:
2020.semeval-1.258
Volume:
Proceedings of the Fourteenth Workshop on Semantic Evaluation
Month:
December
Year:
2020
Address:
Barcelona (online)
Editors:
Aurelie Herbelot, Xiaodan Zhu, Alexis Palmer, Nathan Schneider, Jonathan May, Ekaterina Shutova
Venue:
SemEval
SIG:
SIGLEX
Publisher:
International Committee for Computational Linguistics
Note:
Pages:
1961–1967
Language:
URL:
https://aclanthology.org/2020.semeval-1.258
DOI:
10.18653/v1/2020.semeval-1.258
Bibkey:
Cite (ACL):
Manikandan Ravikiran, Amin Ekant Muljibhai, Toshinori Miyoshi, Hiroaki Ozaki, Yuta Koreeda, and Sakata Masayuki. 2020. Hitachi at SemEval-2020 Task 12: Offensive Language Identification with Noisy Labels Using Statistical Sampling and Post-Processing. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1961–1967, Barcelona (online). International Committee for Computational Linguistics.
Cite (Informal):
Hitachi at SemEval-2020 Task 12: Offensive Language Identification with Noisy Labels Using Statistical Sampling and Post-Processing (Ravikiran et al., SemEval 2020)
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PDF:
https://preview.aclanthology.org/dois-2013-emnlp/2020.semeval-1.258.pdf