LIIR at SemEval-2020 Task 12: A Cross-Lingual Augmentation Approach for Multilingual Offensive Language Identification

Erfan Ghadery, Marie-Francine Moens


Abstract
This paper presents our system entitled ‘LIIR’ for SemEval-2020 Task 12 on Multilingual Offensive Language Identification in Social Media (OffensEval 2). We have participated in sub-task A for English, Danish, Greek, Arabic, and Turkish languages. We adapt and fine-tune the BERT and Multilingual Bert models made available by Google AI for English and non-English languages respectively. For the English language, we use a combination of two fine-tuned BERT models. For other languages we propose a cross-lingual augmentation approach in order to enrich training data and we use Multilingual BERT to obtain sentence representations.
Anthology ID:
2020.semeval-1.274
Volume:
Proceedings of the Fourteenth Workshop on Semantic Evaluation
Month:
December
Year:
2020
Address:
Barcelona (online)
Venue:
SemEval
SIGs:
SIGLEX | SIGSEM
Publisher:
International Committee for Computational Linguistics
Note:
Pages:
2073–2079
Language:
URL:
https://aclanthology.org/2020.semeval-1.274
DOI:
10.18653/v1/2020.semeval-1.274
Bibkey:
Cite (ACL):
Erfan Ghadery and Marie-Francine Moens. 2020. LIIR at SemEval-2020 Task 12: A Cross-Lingual Augmentation Approach for Multilingual Offensive Language Identification. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 2073–2079, Barcelona (online). International Committee for Computational Linguistics.
Cite (Informal):
LIIR at SemEval-2020 Task 12: A Cross-Lingual Augmentation Approach for Multilingual Offensive Language Identification (Ghadery & Moens, SemEval 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.semeval-1.274.pdf
Data
OLID