I 2 C at S em E val-2022 Task 4: Patronizing and Condescending Language Detection using Deep Learning Techniques (2022)

Abstract: Patronizing and Condescending Language is an ever-present problem in our day-to-day lives. There has been a rise in patronizing language on social media platforms manifesting itself in various forms. This paper presents two performing deep learning algorithms and results for the “Task 4: Patronizing and Condescending Language Detection.” of SemEval 2022. The task incorporates an English dataset containing sentences from social media from around the world. The paper focuses on data augmentation to boost results on various deep learning methods as BERT and LSTM Neural Network.

## introduction nowadays, patronizing and condescending language (pcl) (pérez-almendros, espinosa-anke, and schockaert 2022) is used to refer to a forced kindness that derives from a perceived superiority towards another person. a subtle form of bullying, being patronized can leave a person feeling infuriated and impotent. it is a type of behaviour that cuts across generations. an older person can talk down to a younger colleague, but it can just as easily happen the other way around. men can patronize women at work and vice versa. but what they have in common is power play, with one individual exerting their authority or seniority over another. people could feel discriminated by condescending comments. considering the relevance of equality and respect, and it is important to note that nobody should be treated in such a way as to feel intimidated or different. in semeval-2022: task 4 subtask 1, participants must determine whether a phrase presents pcl or not. the idea behind the proposed solution was to compare three models based on deep learning. the first model is a long short-term memory (lstm) neural network @xcite . the second, a lstm neural network with embedding pretrained layer. the third one uses bert @xcite . the latter yielded the best results. for all the models we have used data augmentation to balance the training dataset. the f1-score in our final submission (bert) was 0.4134 on the test dataset. compared to the results of the winning team, the difference is not extremely large. nevertheless, our model obtained a good result of accuracy (0.61) compared to other participants. ## background this paper is focused on subtask 1: binary classification. the corpus provided to perform subtask 1 (pérez-almendros, espinosa-anke, and schockaert 2020) was composed of 10473 documents with 6 features: "id", "doc id", "keyword", "country code", "text" and "label". the dataset was imbalanced with respect to the class "label". out of 10473 rows, 9470 were from the negative class and 993 from the positive class. english data augmentation was applied to the original dataset to balance them (shorten, khoshgoftaar, and furht 2021). ## related work in recent years, several people have been researching this topic. a few years ago, an experiment of the impact of age, race, and stereotypes on perceptions of language performance and patronizing speech was published @xcite . indeed, a research of different types of behaviours in healthcare settings (as condescending language) was published to show the impact it has in the world @xcite . this is a recent problem and there are significant challenges to be overcome. ## experimental setup to set up our models, some libraries were used. some of them were "nltk" @xcite @xcite , "keras" foot_2 , "tensorflow" @xcite , "scikitlearn" @xcite and "pandas" @xcite . to test whether the data was balanced, the original training dataset was used. once the balancing method was decided, the training data was split into two parts -80% for training and 20% for test, using a stratify approach. the stratify parameter makes a split so that the proportion of values in the sample produced will be the same as the proportion of values provided for the parameter to stratify. during the training phase, we evaluated our models with accuracy, roc curve, precision, recall and f1-score measures. once the organizers provided the test data, the models with the original training dataset were trained without splitting. ## results the two models submitted were lstm neural network with pretrained embedding model and bert-base-uncased. according to the official metrics (f1-score for the positive class), a result of 0.413 and 0.61 of accuracy was obtained. bert-base-uncased reached the best results. after training using under sampling and data augmentation methods, we concluded that data augmentation had the best results. ## conclusions in this paper we present our approach and system description on task 4 (subtask 1) in semeval 2022: patronizing and condescending language detection towards communities in the media. the main aim was to develop three deep learning models using data augmentation to solve imbalanced problem of the original dataset. we implemented three different models. after training and analyzing each model, an f1-score of 0.41 in the evaluation process for class "1" was achieved. for future works, we think the models could be further improved by training with a bigger dataset or using more balancing techniques.