hub at S em E val-2021 Task 5: Toxic Span Detection Based on Word-Level Classification (2021)

Abstract: This article introduces the system description of the hub team, which explains the related work and experimental results of our team’s participation in SemEval 2021 Task 5: Toxic Spans Detection. The data for this shared task comes from some posts on the Internet. The task goal is to identify the toxic content contained in these text data. We need to find the span of the toxic text in the text data as accurately as possible. In the same post, the toxic text may be one paragraph or multiple paragraphs. Our team uses a classification scheme based on word-level to accomplish this task. The system we used to submit the results is ALBERT+BILSTM+CRF. The result evaluation index of the task submission is the F1 score, and the final score of the prediction result of the test set submitted by our team is 0.6640226029.

## introduction and background from the popularization of the internet to the first year of the mobile internet in 2011, the number of online social media and social media users has continued to grow. in the context of the ever-expanding user base, coupled with the free and interactive features of online social media communication. therefore, online social media has exposed many issues worthy of our attention, such as the lack of communication standards and the out-of-control of information dissemination, which makes the dissemination of online social media prone to various negative functions @xcite . the task of toxic span detection is to detect the span of text with toxic information in the text @xcite . the goal of the task is to predict the beginning and ending character positions in the text as accurately as possible. reviewing the content in online media can effectively avoid the spread of a series of negative information such as cyber violence, cyberbullying, and false news. audits are essential to promote healthy online discussions. however, the content and number of posts in social media are too large, and the manual review method obviously cannot achieve a good effect. therefore, in combination with the development of modern technology, achieving a semi-automatic audit is the best solution. ## related work there are many different kinds of methods for identifying negative information in social media, but usually, these methods mainly focus on supervised learning. simple surfacefeatures similar to the bag of words model can provide very clear and easy-to-understand information in text processing tasks. the general approach of this method is to merge multiple larger n-grams into a feature set @xcite . use the artificial neural network method to train the word embeddings in the corpus. the purpose is to use the distance between vectors to indicate the semantic similarity of different words. djuric et al. proposed a method of directly using embedding to represent the entire text and showed us the effect of this method @xcite . negative information in the text can be detected by the above methods. but more information needs to be obtained according to the context. the pretrained language model based on the transformer architecture has great advantages both at the word level and in context information @xcite . therefore, in this task, we try to combine the pretrained language model to complete the detection of toxic content. ## data and methods in this section, we introduce the data provided by the task organizer team to the participating teams, as well as the models and methods we use. ## experiment and results in this section, we will introduce the data preprocessing methods and experimental settings we used in the task and the final results. ## conclusion this paper presents the system description submitted by our team to semeval 2021 task 5: toxic spans detection. our goal is to use our system to detect the span of toxic content as accurately as possible. we use a classification scheme based on word-level to complete the task. the system combines the pre-training language model (albert) and bilstm+crf commonly used in nlp tasks. the results we submitted proved the feasibility of our system, but compared with the optimal results, our method still has room for improvement. in future work, we will try to improve our methods to achieve better results.