Hybrid Emoji-Based Masked Language Models for Zero-Shot Abusive Language Detection
Michele Corazza, Stefano Menini, Elena Cabrio, Sara Tonelli, Serena Villata
Abstract
Recent studies have demonstrated the effectiveness of cross-lingual language model pre-training on different NLP tasks, such as natural language inference and machine translation. In our work, we test this approach on social media data, which are particularly challenging to process within this framework, since the limited length of the textual messages and the irregularity of the language make it harder to learn meaningful encodings. More specifically, we propose a hybrid emoji-based Masked Language Model (MLM) to leverage the common information conveyed by emojis across different languages and improve the learned cross-lingual representation of short text messages, with the goal to perform zero- shot abusive language detection. We compare the results obtained with the original MLM to the ones obtained by our method, showing improved performance on German, Italian and Spanish.- Anthology ID:
- 2020.findings-emnlp.84
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2020
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Trevor Cohn, Yulan He, Yang Liu
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 943–949
- Language:
- URL:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2020.findings-emnlp.84/
- DOI:
- 10.18653/v1/2020.findings-emnlp.84
- Cite (ACL):
- Michele Corazza, Stefano Menini, Elena Cabrio, Sara Tonelli, and Serena Villata. 2020. Hybrid Emoji-Based Masked Language Models for Zero-Shot Abusive Language Detection. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 943–949, Online. Association for Computational Linguistics.
- Cite (Informal):
- Hybrid Emoji-Based Masked Language Models for Zero-Shot Abusive Language Detection (Corazza et al., Findings 2020)
- PDF:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2020.findings-emnlp.84.pdf
- Data
- HatEval