CSECU - DSG at S em E val-2023 Task 10: Exploiting Transformers with Stacked LSTM for the Explainable Detection of Online Sexism (2023)

Abstract: Sexism is a harmful phenomenon that provokes gender inequalities and social imbalances. The expanding application of sexist content on social media platforms creates an unwelcoming and discomforting environment for many users. The implication of sexism is a multi-faceted subject as it can be integrated with other categories of discrimination. Binary classification tools are frequently employed to identify sexist content, but most of them provide extensive, generic categories with no further insights. SemEval-2023 introduced the Explainable Detection of Online Sexism (EDOS) task that emphasizes detecting and explaining the category of sexist content. The content of this paper details our involvement in this task where we present a neural network architecture employing document embeddings from a fine-tuned transformer-based model into stacked long short-term memory (LSTM) and a fully connected linear (FCL) layer . Our proposed methodology obtained an F1 score of 0.8218 (ranked 51st) in Task A. It achieved an F1 score of 0.5986 (ranked 40th) and 0.4419 (ranked 28th) in Tasks B and C, respectively.

## introduction the ongoing progress and expansion of technology and social platforms are driving the growth of user-generated content. online platforms (e.g., facebook, twitter, blogs, and forums) reflect a wide range of perspectives, facts, and experiences the first three authors have equal contributions. from a diverse set of people. in addition, social media, which offers an anonymous atmosphere, also enables users to display hostile sentiments that can result in harassment of individuals with various genders, nations, races, cultures, and physical features @xcite . online sexism is a rising issue nowadays. sexism is a prevalent form of hate speech that is presently seen as a negative trend on social media. sexism involves the unjust and discriminatory treatment of women or holding negative attitudes towards them solely because of their gender or because of their gender in combination with other aspects of their identity, such as their race, religion. @xcite @xcite . automatic approaches @xcite @xcite @xcite are increasingly used to find and assess sexist content at scale, however, most of them just classify information into general, high-level categories without offering any further explanation. the ability to identify sexist content and to explain why it is sexist enhances the interpretability, trust, and comprehension of the choices made by automated systems, giving users and moderators more control. to address this scope, a shared task was introduced at semeval-2023 by @xcite to facilitate the evolution of more precise and explainable english-language models for detecting sexism, including fine-grained classifications for sexist content. the task is hierarchically structured into three sub-tasks. in this study, we expound our approach to address the challenges posed by the edos task. the key contribution of this paper is our proposal to utilize the bertweet (nguyen et al., 2020a) model with the accumulation of features from stacked lstm @xcite and a fully connected layer (fcl). this approach allows us to leverage the strengths of each component: bertweet furnishes semantically meaningful text embeddings, lstm captures long-term dependencies, and the fcl selects and encapsulates the most effective contextual features. the subsequent sections of this manuscript are arranged in the following manner: section 2 pro-vides an overview of related work, whereas section 3 elucidates our proposed framework. in section 4, we explicate our experimental settings and present a performance analysis of our model. finally, we draw our paper to a close in section 5. ## related work sexism is defined as any violence or hate speech directed against women that are motivated by their gender alone, or by their gender in combination with one or more other identifying characteristics @xcite . the last ten years have witnessed a surge in the amount of sexist and derogatory content directed toward women on social media. disclosure of sexist speech has negative effects on the lives of women and restricts their right to give free opinions. prior research has concentrated on identifying bias against or violence against women @xcite . the majority of relevant nlp research is particularly concerned with detecting abusive language. even though overt sexism appears to be simple to identify, it is hidden nuances and varied presentations are not. in recent days, automated sexism detection techniques have been the subject of research @xcite . scholars have focused on confrontational and overt sexism, ignoring subtle or implicit sexism. several datasets were created to research sexism, which includes the display of hatred or hostility toward women @xcite . other datasets concentrated on the many categories of sexism @xcite as well as other media that depict the various types of sexism @xcite . the automatic misogyny detection of the ibereval competition in the twitter section focused on the automatic recognition of sexism @xcite . the dataset for semeval-2019 task 5 was likewise constructed using the data from this contest @xcite . the many strategies that were put out for the competition were primarily focused on supervised machine learning on various textual features, such as n-grams @xcite , sentiment and syntactic data, and user-based contents, such as the number of repeated tweets, etc. on the other hand, numerous lexical resources @xcite , including swear words and sexist insults, are used, along with deep learning techniques @xcite ) like recurrent neural networks and word embedding features, to obtain the final labels. the detection of hate speech is connected to recent research on the labeling of sexism. for identifying hate speech, there are numerous methods available @xcite . methods encompassed bag-of-words techniques, machine learning-based classifiers @xcite , standard machine learning-based approaches including support vector machines, logistic regression, and decision trees @xcite , as well as sentiment and lexical-based features. over the years, deliberation has been drawn to the use of transfer learning-based techniques and neural models for the detection of hate speech. typically, these models used deep learning strategies like cnn and lstm networks @xcite , which performed remarkably well on the diverse tasks of natural language processing. briefly said, we observed that the majority of researchers examined the traditional strategies in their suggested methodologies, which included a collection of different preprocessing techniques, manually created features, and statistical classifiers where transformer-based models including roberta, bert, albert, etc. play a vital role as models built on transformers are effective at understanding sentence context. however, it capitulates some specific context learning whereas in our proposed approach we amalgamated transformer document embeddings with stacked lstm and an fcl layer. finally, this merged form is exploited to an fcl output layer to obtain the final prevision labels. transformer document embeddings with staked lstm extract diverse context of a single sentence that helps us to procure a competitive performance in all the subtasks of semeval-2023 task 10. ## proposed method our proposed framework is focused on distinguishing interpretable instances of sexism from informal user-generated text. figure 1 illustrates a graphical depiction of our proposed framework. we extract document embedding features for a given text incorporating the transformer model bertweet. we leverage the flair @xcite to generate the document embeddings. extracted document embeddings vector is passed through two distinct segments. the first one is stacked lstm and the other is an fcl. later, the features from the stacked lstm and the fcl are accumulated through concatenation to generate the final text representation. the final accumulated features combination is fed to a fully-connected output layer to obtain predicted labels. ## conclusion we presented our methodology for the semeval-2023 edos task in this publication. we extracted transformer-based document embeddings and integrated them with stacked lstm and an fcl layer. our proposed approach obtained compar-ative performance in all the subtasks. transformerbased document embeddings can capture the intricate word relationships in a sentence, and stacked lstm can extract the deep-level patterns of embeddings which helps our model to improve its performance. the inclusion of additional features or data sources could enhance the performance of our approach. we also plan to explore parameter tunings more meticulously, that can improve our system for multiclass classification.