Team LEGO at S em E val-2022 Task 4: Machine Learning Methods for PCL Detection (2022)

Abstract: In this paper, we present our submission to the SemEval 2022 - Task 4 on Patronizing and Condescending Language (PCL) detection. Weapproach this problem as a traditional text classification problem with machine learning (ML)methods. We experiment and investigate theuse of various ML algorithms for detecting PCL in news articles. Our best methodology achieves an F1- Score of 0.39 for subtask1 witha rank of 63 out of 80, and F1-score of 0.082for subtask2 with a rank of 41 out of 48 on the blind dataset provided in the shared task.

## introduction the explosion of social media in recent years also enables increasing the number of patronizing and condescending language (pcl). patronizing and condescending language depicts apparently kind or helpful behavior but betraying a feeling of superiority on others. previously, harmful behavior in language for example, hate speech @xcite , offensive language @xcite , fake news @xcite , rumor propagation or misinformation @xcite , and many others has been widely studied in nlp, pcl has been a neglected area of study until very recently. identifying pcl is hard even for humans because it is subjective and subtle. for instance, one might find condescending something which another person might consider an objective portrayal of a situation or some people might not see the harm in describing how those in a privileged position donate their remainings to those who need them. also, we would expect a member of a so-called vulnerable community to feel more patronised than one person who does not belong to such group while reading how others refer to them. the goal of semeval 2022-task 4 is to design a system to detect whether or not the text contains any form of pcl and furthermore, identify which pcl category expresses the condescension. the organizers provided two datasets, one annotated based on the intensity of pcl and other with the pcl categories. we approach this problem using various machine learning approaches using the linguistics features. the structure for the rest of the paper is as follows. section 2 describes a background about the dataset. section 3 describes the experimental setup of our experiments. it involves pre-processing, feature engineering, implementation details for all the respective ml models. section 4 describes the results and discussion for both the subtask. and lastly, in section 5, we concluded the paper and suggest ideas for future research. ## dataset the dataset (perez-almendros et al., 2020) provided for this challenge was collected from the news on web (now) corpus @xcite . for task1, we are provided with 10469 text paragraphs. each paragraph instance in the dataset is provided with paragraph-level label, vulnerable communityinfo which includes (disabled, homeless, hopeless, immigrant, in-need, migrant, poor families, refugee, vulnerable and women), and along country of origin. there are 5 classes (0-4) based on the intensity of pcl. for task2, we are provided with 993 paragraphs. each paragraph instance in the dataset is provided with keyword, country of origin, span-text, category label, and number of agreeing annotators. the labels comprise of 7 classes: unbalanced power relations(unb) , shallow solution(shall), presupposition(pres), authority voice(auth), metaphor(meta), compas- ## results & discussion we evaluate and report the performance of different models on our validation set and the blind-test set ( ## conclusion this paper presents our study of machine learning models for the binary and multi-label text classification on the pcl detection shared task. we find that tf-idf features can be effective in cases where train and testing data are from the same distribution but it may fail otherwise. for future work, we plan to experiment with contextual embeddings from bert, and other transformer-based models. we also would like to look into bootstrapping and data augmentation techniques to solve the class imbalance problem more effectively.