DH - FBK at S em E val-2023 Task 10: Multi-Task Learning with Classifier Ensemble Agreement for Sexism Detection (2023)

Abstract: This paper presents the submissions of the DH-FBK team for the three tasks of Task 10 at SemEval 2023. The Explainable Detection of Online Sexism (EDOS) task aims at detecting sexism in English text in an accurate and explainable way, thanks to a fine-grained annotation that follows a three-level schema: sexist or not (Task A), category of sexism (Task B) and vector of sexism (Task C) exhibited. We use a multi-task learning approach in which models share representations from all three tasks, allowing for knowledge to be shared across them. Notably, with our approach a single model can solve all three tasks. In addition, motivated by the subjective nature of the task, we incorporate inter-annotator agreement information in our multi-task architecture. Although disaggregated annotations are not available, we artificially estimate them using a 5-classifier ensemble, and show that ensemble agreement can be a good approximation of crowd agreement. Our approach achieves competitive results, ranking 32nd out of 84, 24th out of 69 and 11th out of 63 for Tasks A, B and C respectively. We finally show that low inter-annotator agreement levels are associated with more challenging examples for models, making agreement information use ful for this kind of task.

## introduction the prevalence of sexism on the internet can be damaging to the women it targets and make online spaces hostile. in order to address this, automated methods are often used to identify sexist content on a large scale, though typically only high-level categories are identified, without further explanation. improved interpretability and understanding of the decisions made by artificial intelligence when flagging what is sexist and why it is sexist could empower both users and moderators. recently, the @xcite ) "explainable detection of online sexism" (edos) has been proposed, to encourage research on sexist language detection in a more accurate and explainable way. this shared task focuses on exploring methods for identifying explicit sexism and classifying the type of behavior expressed into fine-grained categories in english data from gab and reddit. in this paper, we present the dh-fbk entries for the three tasks of edos. we propose a multitask learning approach, a paradigm that leverages training signals of related tasks at the same time by exploiting a shared representation in the model. in particular, we simultaneously train models on all three tasks, corresponding to the three levels of the annotation schema utilized. the hypothesis is that the annotation levels contain mutually relevant information, making knowledge sharing beneficial for the execution of each task. furthermore, motivated by the subjectivity of the task at hand and in connection to our research interests, we use an additional auxiliary task in the multitask configuration. previous research @xcite @xcite has showed how in an offensive language detection task, training on data with low levels of inter-annotator agreement can be detrimental for model performance. however, recent research has also showed that this depends on the reason for disagreement, and that it can be helpful to include information about agreement level when training @xcite . as disaggregated annotations are not available for participants to use, we artificially estimate a measure of agreement by using an ensemble of five classifiers to mimic annotator judgments on train set items. we then use the level of agreement between classifiers as a proxy for inter-annotator agreement, and exploit this information in one of the tasks we train our models on. moreover, in the analysis section of the paper, we discuss the impact of the auxiliary agreement task on training, by showing a number of additional experiments in which we compare the effect of using synthetic agreement information with that of using actual gold crowd agreement information (released by organizers after competition end) and the effect of removing data with low agreement from training. furthermore, we discuss impact of agreement on test set, by showing how selecting test data according to different levels of annotators' agreement has a strong effect on classifiers performance. our method achieves competitive results on the semeval-2023 task 10, ranking well in all the tasks, especially the ones concerning the more finegrained classifications. with our approach, a single model would suffice for achieving reasonably good performance on all three tasks. however, we used two models for our submission, given that the task was designed so that the fine-grained tasks had smaller test sets. ## related work currently, the definition of sexism and misogyny is under discussion (see for example @xcite ). one of the most widely accepted definitions of misogyny involves the expression of hostility and hatred towards women. in contrast, sexism comprises any form of oppression or prejudice against women, ranging from more subtle language to overtly hostile (as in the case of misogyny). research on online misogyny and sexism is relatively recent and in fast development. one of the first attempts is by waseem and hovy (2016). in the context of a broader investigation on online hate speech, they categorised misogyny as a sub-branch of hate speech ("hate towards women") and dedicated a part of their hate-speech dataset to this. interestingly, authors claim that annotating misogyny would require more than a binary present/absent label. in the same year, @xcite collected the first dataset annotated for misogynistic content. they aimed to highlight the challenges of identifying misogynist abuse, rather than finding examples of abusive tweets and analysed the usage of the keywords such as cunt, slut and bitch, and revealed how 'they have crept into general use as a form of address', highlighting how the problem of defining what constitutes misogynist language remains. research on automatic misogyny identification further attracted attention when @xcite proposed the first taxonomy of misogynistic language behaviors in social media (five categories: discredit, harassment & threats of violence, derailing, stereotype & objectification, and dominance). in the same year, anzovino and colleagues organised two shared tasks for the evaluation of systems performing automatic misogyny identification (ami) in english, italian, and spanish @xcite with benchmarks annotated on two levels, using the proposed taxonomy. after this first attempt to categorise misogyny into different types, other research works have proposed solutions along the same line, creating new datasets, extending taxonomies and adding new languages (e.g. rodríguez-sánchez et al. (2020); zeinert et al. (2021); almanea and poesio (2022)). ## data and task description the explainable detection of online sexism (edos) task is focused on the detection of sexism beyond high level categories. sexism, defined for the annotators of this corpus as "any abuse or negative sentiment that is directed towards women based on their gender, or based on their gender combined with one or more other identity attributes" @xcite , is in fact often identified by automatic systems in a binary fashion, with tools simply flagging online content as sexist or not sexist, providing no further details or explanations. content moderation based on this kind of tools, however, can often result in poor explainability and interpretability @xcite . the goal of the edos task is that of developing models for detecting sexism in english that are both more accurate and more explainable, thanks to more fine-grained annotations in the training data. ## methods given the hierarchical nature of the taxonomy of the edos task, there are strong correlations among the three subtasks. for instance, identifying the presence of a vector in a post (task c) necessarily implies both identifying a specific category of sexism (task b) and identifying a post as sexist (task a). because of this, we aim at leveraging the information shared across the annotation levels with multi-task learning, a paradigm that aims at exploiting training signals of related tasks at the same time by using a shared representation in the model @xcite . in addition to exploiting the information shared across the levels of annotation for the three subtasks, we aim at leveraging information on how potentially ambiguous or challenging a post can be. given that during the participation in the edos task we had no information on inter-annotator agreement level or disaggregated annotation for each post, we "artificially" created an agreement label for each post using an ensemble of 5 classifiers, which we refer to as ensemble agreement. the idea of exploiting classifier agreement follows the finding by @xcite that classifier ensembles can be useful for identifying ambiguous or challenging posts in the context of offensive language detection. we describe the process we followed for assigning ensemble agreement labels in section 4.1. ## experiments in this section, we first outline the experimental setup, then we present the results of our models on the three tasks. we finally show some additional analyses and discussion. ## conclusion in this paper, we described the systems submitted for edos semeval-2023 task 10. we utilize a multi-task learning approach which allows our models to share representations across all three tasks of edos. additionally, due to the subjective nature of this task, we include inter-annotator agreement information into our multi-task framework. as disaggregated annotations are not available, we artificially estimate them with a 5-classifier ensemble. our system achieved competitive results on the official leaderboard ranking in upper half in all three tasks, without relying on any external data. moreover, we conducted a thorough analysis on the role and impact of agreement on task a ( we hypothesise that agreement could play an important role on classification of finer-grained labels for sexism (task b and c), given that for higher levels of accuracy (and higher number of options available for annotation), more personal sensitivity comes into play. we leave this aspect for the near future investigation. finally, data for which annotators failed to reach unanimous consensus are around 22% of the current dataset, and represent an important challenge in terms of classification performance, in part due to the fact that they represent subjective points of view in judgements that are legitimate in this type of tasks. our hope is that our work can contribute to the debate about the importance of having different points of view, rather than disregarding them, when compiling datasets and designing classifiers.