Abstract
The paper presents a system developed for the SemEval-2020 competition Task 12 (OffensEval-2): Multilingual Offensive Language Identification in Social Media. We achieve the second place (2nd) in sub-task B: Automatic categorization of offense types and are ranked 55th with a macro F1-score of 90.59 in sub-task A: Offensive language identification. Our solution is using a stack of BERT and LSTM layers, training with the Noisy Student method. Since the tweets data contains a large number of noisy words and slang, we update the vocabulary of the BERT large model pre-trained by the Google AI Language team. We fine-tune the model with tweet sentences provided in the challenge.- Anthology ID:
- 2020.semeval-1.280
- 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:
- 2111–2116
- Language:
- URL:
- https://aclanthology.org/2020.semeval-1.280
- DOI:
- 10.18653/v1/2020.semeval-1.280
- Cite (ACL):
- Bao-Tran Pham-Hong and Setu Chokshi. 2020. PGSG at SemEval-2020 Task 12: BERT-LSTM with Tweets’ Pretrained Model and Noisy Student Training Method. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 2111–2116, Barcelona (online). International Committee for Computational Linguistics.
- Cite (Informal):
- PGSG at SemEval-2020 Task 12: BERT-LSTM with Tweets’ Pretrained Model and Noisy Student Training Method (Pham-Hong & Chokshi, SemEval 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.semeval-1.280.pdf