NLP _ UIOWA at S emeval-2021 Task 5: Transferring Toxic Sets to Tag Toxic Spans (2021)

Abstract: We leverage a BLSTM with attention to identify toxic spans in texts. We explore different dimensions which affect the model’s performance. The first dimension explored is the toxic set the model is trained on. Besides the provided dataset, we explore the transferability of 5 different toxic related sets, including offensive, toxic, abusive, and hate sets. We find that the solely offensive set shows the highest promise of transferability. The second dimension we explore is methodology, including leveraging attention, employing a greedy remove method, using a frequency ratio, and examining hybrid combinations of multiple methods. We conduct an error analysis to examine which types of toxic spans were missed and which were wrongly inferred as toxic along with the main reasons why they occurred. Finally, we extend our method via ensembles, which achieves our highest F1 score of 55.1.

## introduction social media platforms (e.g. twitter) benefit from a welcoming environment. toxicity introduced by users can drive new (or existing) users away. therefore, it is beneficial for platforms to moderate the amount of toxic language allowed. often human moderators are employed to address this. however, as the numbers of users increase on a platform, it can be difficult for human moderation as the sheer amount of content generated increases as well. thus platforms employ automated assistance @xcite . while automated toxic language classifiers have made strides at determining if text is toxic @xcite , much work is still left for humans who are reviewing the text. in large texts, the reason for toxicity might only be one or two words, however, the moderator will have to ## approach we expand on the approach we use to identify toxic spans. we examine both different toxic based datasets as well as different blstm based methods. ## error analysis to determine how to improve our system, we examine the spans commonly missed in the test set as well as spans commonly predicted wrongly. we examine errors in our top system (toxic train -precison hybrid). ## leveraging multiple views via ensemble of datasets as toxicity can vary, it follows that different toxic datasets can identify different aspects of toxic spans. we explore this idea further by creating a simple majority voting ensemble model. the ensemble model takes in the predictions of multiple models trained on different datasets, then indices are included if a simple majority votes for the index. a summary of the performance of these models is found in ## related work we provide a brief look at work related to ours and highlight recent related ideas. toxic language: in recent years toxic language classification has seen much focus. organized tasks for offensive language classification were seen multiple years in a row in germeval @xcite , offenseval 2019 @xcite , and offenseval 2020 @xcite . additionally, hate and abuse have been the focus of classification @xcite . for toxicity specifically, @xcite examine detecting threads that could lead to toxic language in the future. @xcite present specific challenges that remain for toxic classification. methodology: our work draws from both robustness and text style transfer work. robustness work looks at attacks of adversaries against classification (and other) systems. hsieh et al. (2019) look at the robustness of attention based systems. their "greedy-select" method for selecting words to change in obfuscation, is similar to our "greedyremove" method. style transfer often focuses on transferring text from one style to another. to change the style, some authors first remove or mask words which indicate the initial style from the text. @xcite accomplish this by leveraging both a frequency ratio method (same as ours) and attention. though, the methodology is similar, we examine it in terms of toxic span identification where it has not been done before. ## conclusion we leveraged various blstm methods to identify toxic spans. we find that a combination of a "greedy-remove" and "attention" based methods with a focus on precision produces the highest overall results. we examined how well various toxic related datasets are able to transfer to this task. we find that olid, an offensive labeled dataset, transfers the highest, achieving an f1 only 0.6 lower than the provided dataset. furthermore, we found that combining the viewpoints of the datasets in an ensemble model can increase the performance (f1 to 55.1). finally, we examined the errors our system made and the causes. we found that many errors were made when translating from tokens to character index. future work would improve on this translation step to tighten the span indication.