U ni B oe’s at S em E val-2023 Task 10: Model-Agnostic Strategies for the Improvement of Hate-Tuned and Generative Models in the Classification of Sexist Posts (2023)

Abstract: We present our submission to SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS). We address all three tasks: Task A consists of identifying whether a post is sexist. If so, Task B attempts to assign it one of four categories: threats, derogation, animosity, and prejudiced discussions. Task C aims for an even more fine-grained classification, divided among 11 classes. Our team UniBoe’s experiments with fine-tuning of hate-tuned Transformer-based models and priming for generative models. In addition, we explore model-agnostic strategies, such as data augmentation techniques combined with active learning, as well as obfuscation of identity terms. Our official submissions obtain an F1_score of 0.83 for Task A, 0.58 for Task B and 0.32 for Task C.

## introduction the shared task on explainable detection of online sexism (edos) defines sexism as any abuse or negative sentiment directed towards women based on their gender, or based on their gender combined with one or more other identity attributes (e.g. black women, muslim women, trans women) @xcite . the edos shared task focuses on english posts from reddit and gab and proposes three hierarchical sub-tasks. task a binary sexism detection: systems have to predict whether a post is sexist or not. task b category of sexism: if a post is sexist, systems have to predict one of four mutually exclusive categories: (1) threats, (2) derogation, (3) animosity, or (4) prejudiced discussions. task c fine-grained vector of sexism: if a post is sexist, systems have to predict one among 11 mutually exclusive subcategories, e.g., threats of harm, descriptive attacks (see @xcite . in this paper, we present our approach to address all three subtasks. for task a and b, we employ hate-tuned models built upon bert @xcite and roberta @xcite . we experiment with two model-agnostic techniques: obfuscation of identity terms and data augmentation with and without active learning. these strategies apply only to the model inputs and not to the internal model structure. the first strategy involves masking genderidentifying information such as names and pronouns and aims at reducing unintended bias @xcite . the second strategy uses feedback from the model to iteratively select new training examples that positively influence the performance on the validation set. for task c we employ a single hate-tuned model, roberta-hate, and explore the potential of generative models in such a fine-grained text classification setting via in-context learning. ## background sexist language can take many forms, ranging from overtly misogynistic or violent language to subtle forms, such as implicit bias @xcite and microaggressions @xcite . while misogyny implies hate towards women @xcite , sexism can be concealed behind friendly statements, like in benevolent sexism @xcite , making sexism detection challenging. while many relevant shared tasks have been focusing on the detection of misogyny @xcite @xcite @xcite , some have tackled the detection of sexism as well; i.e. the two editions of sexism identification in social networks @xcite , which focused both on a binary and a multi-class categorization of sexism. in both editions, the majority of participants exploited transformer-based systems for both tasks. some managed to improve the performance with data augmentation techniques, via back translation tech-niques @xcite or task-related existing datasets @xcite . sexism detection has been addressed mostly as a binary or a multi-class problem by identifying the type of sexist behaviours @xcite @xcite . some strategies leverage knowledge graphs @xcite , ensemble models of neural architectures @xcite , lstms @xcite or cnns @xcite . @xcite handles this problem as a style transfer task, by turning implicit bias in language into explicit statements. @xcite explore bert contextualized word embeddings complemented with both linguistic features and generalization strategies (i.e., replacement combinations) in order to distinguish reported sexist acts from real sexist messages. one of our objectives in this shared task is to explore the use of generative models for the identification of types of sexism. nlp has experienced a surge in promising generative models such as @xcite , gpt-neo @xcite and bart @xcite . they have shown to possess a broad set of language pattern recognition abilities, which are employed during the forward-pass to adapt to any task on the fly, including text classification. this method of classification is called prompting @xcite , and while many variations exist, one particularly successful technique is priming @xcite . also known as in-context learning or fewshot classification, it consists in prepending a limited amount of examples to the message to be predicted, additionally wrapping each one in a template. since both the learning and prediction steps coincide, there is no requirement for further weight updates @xcite . to the best of our knowledge, their usage in the field of hate speech detection is limited. @xcite examined the ability of gpt-3 to identify hate speech on the ethos dataset @xcite . their findings show that these models are not ideal for hate speech detection, as the average accuracy rates are between 50 and 70% in a binary setting. in addition to labelled data, 2m unlabelled posts were provided -1m from gab and 1m from reddit-, which were used to augment our training set through active learning @xcite . ## models description we experiment with hate-tuned transformer-based models and generative models. we compare the former with their original counterpart: bert @xcite and roberta @xcite . all hate-tuned models are fine-tuned on our downstream task. we perform a minimum parameter selection tuning on the validation set (10% of the training set). we selected the highest performing learning @xmath0, 2e-5, 1e-2]; batch @xmath1, 8, 16, 32]; epochs in range [1 -10]. appendix a includes the best parameters for each model. ## experiments and results in this section we present the experiments performed for each of the tasks, along with the results on the development and test set. ## error analysis we conduct an error analysis for task a to understand patterns of misclassification. 2 we observe all misclassified instances manually. with the help of the nltk library, we retrieve frequent words in misclassified instances. in false positive cases in task a we found identity terms (e.g., women) and sexually-abusive terms such as rape and f%ck, used without the intentionality of harming, like in the case of reports of sexist acts or non-offensive slurs. since such terms are frequently used in sexist instances, the model gets confused when they occur in non-sexist instances, resulting in being labeled as sexist. this suggests that intentionality must be considered when discriminating between actually sexist and reported sexist acts, as stressed by @xcite . the obfuscation of identity terms led to a decrease in false positive rates, at the expenses of a lower recall, resulting in more false negatives. this is expected, as for instance the sentence 'i hate women' would be likely identified by a standard model, but the sentence 'i hate [them]' would likely not be classified as sexist. ## conclusions in this paper we presented our submission to the edos shared task @xcite . we experimented with hate-tuned transformer-based models for task a, b and c, and generative models for task b and c. for task a, we adopted modelagnostic strategies such as the obfuscation of identity terms and data augmentation, with and without active learning. for all three tasks, our best model is always a vanilla roberta-hate. for task a, the model does not benefit from additional data, neither task-related annotated data, nor silver data produced via active learning. the obfuscation of identity terms does not negatively impact the performance, but does not help either, although we manage to decrease the false positive rates, at the expense of a lower recall. for task c, deep learning models do not have enough samples to learn from: a hybrid linguistically-informed system might thus be preferable for this kind of task and it is our intention to try it in the future.