Abstract
In a live streaming chat on a video streaming service, it is crucial to filter out toxic comments with online processing to prevent users from reading comments in real-time.However, recent toxic language detection methods rely on deep learning methods, which can not be scalable considering inference speed.Also, these methods do not consider constraints of computational resources expected depending on a deployed system (e.g., no GPU resource).This paper presents an efficient method for toxic language detection that is aware of real-world scenarios.Our proposed architecture is based on partial stacking that feeds initial results with low confidence to meta-classifier.Experimental results show that our method achieves a much faster inference speed than BERT-based models with comparable performance.- Anthology ID:
- 2022.emnlp-industry.58
- Volume:
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, UAE
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 571–578
- Language:
- URL:
- https://aclanthology.org/2022.emnlp-industry.58
- DOI:
- Cite (ACL):
- Yuto Oikawa, Yuki Nakayama, and Koji Murakami. 2022. A Stacking-based Efficient Method for Toxic Language Detection on Live Streaming Chat. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 571–578, Abu Dhabi, UAE. Association for Computational Linguistics.
- Cite (Informal):
- A Stacking-based Efficient Method for Toxic Language Detection on Live Streaming Chat (Oikawa et al., EMNLP 2022)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.emnlp-industry.58.pdf