HITSZ - HLT at S em E val-2021 Task 5: Ensemble Sequence Labeling and Span Boundary Detection for Toxic Span Detection (2021)

Abstract: This paper presents the winning system that participated in SemEval-2021 Task 5: Toxic Spans Detection. This task aims to locate those spans that attribute to the text’s toxicity within a text, which is crucial for semi-automated moderation in online discussions. We formalize this task as the Sequence Labeling (SL) problem and the Span Boundary Detection (SBD) problem separately and employ three state-of-the-art models. Next, we integrate predictions of these models to produce a more credible and complement result. Our system achieves a char-level score of 70.83%, ranking 1/91. In addition, we also explore the lexicon-based method, which is strongly interpretable and flexible in practice.

## introduction our system achieves a char-level score of 70.83%, ranking 1/91. besides, we also explore the lexicon-based methods, which usually have high precision but rather low recall, and are strongly interpretable and flexible in practice. first, we mine a toxic lexicon from the training set by a simple statistical strategy. next, wordnet @xcite and glove @xcite are utilized to extend this lexicon further. with a toxic lexicon, we extract toxic spans through word-level matching. ## related work in recent years, cyber violence has become a widespread societal concern, and how to identify and filter hate speech has become an important topic in machine learning. trac proposes an aggression recognition task @xcite ) that provides a dataset of 15,000 annotated facebook posts and comments in english and hindi for training and validation. the task aims to classify comments into three categories: non-aggressive, covertly aggressive, and overly aggressive. the toxic comment classification challenge 5 2 is an open competition in kaggle that provides participants with comments from wikipedia and defines six toxic categories: toxic, severe toxic, obscene, threat, insult, identity hate. in semeval 2019 task 6 @xcite , in addition to whether the comment is offensive, the type of the attack and the target of the attack are also included. based on this, semeval 2020 task 12 @xcite further extends the dataset to 5 languages: arabic, danish, english, greek, and turkish. ## methods in the section, we describe how toxic span detection is formalized and corresponding solutions in detail. ## conclusion in this paper, we formalize the toxic span detection as two problems separately and employ three stateof-the-art models. the strengths of each model are analyzed and a more credible and complement result is obtained through a voting approach. our re-sults achieve a good score (ranking 1/91). besides, we explore a lexicon-based approach. the lexicon is mined from the annotation of the training data and then expanded by wordnet and glove. experiments show that the lexicon-based approach has not yet achieved the performance of the ensemble approach. we believe that future work could move towards combining deep learning-based methods and lexicon-based methods.