%0 Conference Proceedings %T S-NLP at SemEval-2021 Task 5: An Analysis of Dual Networks for Sequence Tagging %A Nguyen, Viet Anh %A Nguyen, Tam Minh %A Quang Dao, Huy %A Huu Pham, Quang %Y Palmer, Alexis %Y Schneider, Nathan %Y Schluter, Natalie %Y Emerson, Guy %Y Herbelot, Aurelie %Y Zhu, Xiaodan %S Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021) %D 2021 %8 August %I Association for Computational Linguistics %C Online %F nguyen-etal-2021-nlp %X The SemEval 2021 task 5: Toxic Spans Detection is a task of identifying considered-toxic spans in text, which provides a valuable, automatic tool for moderating online contents. This paper represents the second-place method for the task, an ensemble of two approaches. While one approach relies on combining different embedding methods to extract diverse semantic and syntactic representations of words in context; the other utilizes extra data with a slightly customized Self-training, a semi-supervised learning technique, for sequence tagging problems. Both of our architectures take advantage of a strong language model, which was fine-tuned on a toxic classification task. Although experimental evidence indicates higher effectiveness of the first approach than the second one, combining them leads to our best results of 70.77 F1-score on the test dataset. %R 10.18653/v1/2021.semeval-1.120 %U https://aclanthology.org/2021.semeval-1.120 %U https://doi.org/10.18653/v1/2021.semeval-1.120 %P 888-897