SINAI at SemEval-2021 Task 5: Combining Embeddings in a BiLSTM-CRF model for Toxic Spans Detection
Flor Miriam Plaza-del-Arco, Pilar López Úbeda, Luís Alfonso Ureña López, María Teresa Martín Valdivia · 2021
This paper describes the participation of SINAI team at Task 5: Toxic Spans Detection which consists of identifying spans that make a text toxic.Although several resources and systems have been developed so far in the context of offensive language, both annotation and tasks have mainly focused on classifying whether a text is offensive or not.However, detecting toxic spans is crucial to identify why a text is toxic and can assist human moderators to locate this type of content on social media.In order to accomplish the task, we follow a deep learning-based approach using a Bidirectional variant of a Long Short Term Memory network along with a stacked Conditional Random Field decoding layer (BiLSTM-CRF).Specifically, we test the performance of the combination of different pre-trained word embeddings for recognizing toxic entities in text.The results show that the combination of word embeddings helps in detecting offensive content.Our team ranks 29th out of 91 participants.