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

arXiv: https://arxiv.org/abs/2005.03695 · HTML: https://mlsanity.com/paper/2005.03695/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2020.

## Built on by (most cited first, 5 of 5)

- [Cross-lingual Offensive Language Identification for Low Resource Languages: The Case of Marathi](https://mlsanity.com/paper/2109.03552/) (2021)
- [Multilingual Offensive Language Identification for Low-resource Languages](https://mlsanity.com/paper/2105.05996/) (2021)
- [Predicting the Type and Target of Offensive Social Media Posts in Marathi](https://mlsanity.com/paper/2211.12570/) (2022)
- [LIIR at SemEval-2021 task 6: Detection of Persuasion Techniques In Texts and Images using CLIP features](https://mlsanity.com/paper/2105.14774/) (2021)
- [When Do Discourse Markers Affect Computational Sentence Understanding?](https://mlsanity.com/paper/2309.00368/) (2023)

## Abstract (arXiv)

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. LIIR achieved rank 14/38, 18/47, 24/86, 24/54, and 25/40 in Greek, Turkish, English, Arabic, and Danish languages, respectively.