GUTS at S em E val-2022 Task 4: Adversarial Training and Balancing Methods for Patronizing and Condescending Language Detection (2022)

Abstract: Patronizing and Condescending Language (PCL) towards vulnerable communities in general media has been shown to have potentially harmful effects. Due to its subtlety and the good intentions behind its use, the audience is not aware of the language’s toxicity. In this paper, we present our method for the SemEval-2022 Task4 titled “Patronizing and Condescending Language Detection”. In Subtask A, a binary classification task, we introduce adversarial training based on Fast Gradient Method (FGM) and employ pre-trained model in a unified architecture. For Subtask B, framed as a multi-label classification problem, we utilize various improved multi-label cross-entropy loss functions and analyze the performance of our method. In the final evaluation, our system achieved official rankings of 17/79 and 16/49 on Subtask A and Subtask B, respectively. In addition, we explore the relationship between PCL and emotional polarity and intensity it contains.

## introduction patronizing and condescending language (pcl) expresses a superior attitude towards vulnerable communities (e.g. women, refugees, poor families), and describes them or their situation in a charitable way that evokes feelings of compassion @xcite . although it is generally used involuntarily and with good intentions, the use of pcl can potentially be very harmful, as it feeds stereotypes, routinizes discrimination and drives to greater exclusion. due to the subtlety of pcl, pcl detection is difficult for both humans and nlp systems and has aroused broad attention. to address the challenge of patronizing and condescending language detection in general media, @xcite introduce the task 4 at semeval-2022, and build a dataset with annotated paragraphs extracted from news articles in english. given a paragraph, systems must predict whether it contains condescending language or not (subtask a), and whether it contains any of the 7 subtypes identified in the pcl taxonomy @xcite . for subtask a, a binary classification task, we introduce adversarial training based on fast gradient method (fgm) @xcite , enhancing the robustness of the model. and in subtask b, a multi-label classification problem, there is a long-tailed distribution of each label. to address the class imbalance problem, we utilize various improved multi-label cross-entropy loss functions: focal loss @xcite , class-balanced focal loss @xcite and distribution-balanced loss @xcite . we analyze the performance of our methods and demonstrate the contribution of each component of the architecture. in addition to completing basic evaluation tasks, we also explore the relationship between pcl and emotional polarity and intensity it contains in official dataset. the experimental results demonstrate that the above two have relevance. the structure of the paper is as follows: we first provide a brief overview of related research, and then introduce our proposed framework. besides, experiments and evaluations as well as the analysis of results are given. finally, we discuss the future directions of our work. ## related work patronizing and condescending language has been studied extensively in sociolinguistics and the traits of pcl have been suggested by related research. pcl builds stereotypes @xcite , which strengthen exclusion, discrimination, rumour spreading @xcite and unbalanced power relations @xcite , relying on subtle language @xcite . it tends to avoid stating the reasons for deep-rooted societal problems by concealing those responsible and proposes temporary solutions @xcite , which oversimplify the core problems @xcite . the abuse of pcl exacerbates the difficulty of improving the lives of disadvantaged groups @xcite and dehumanizes minorities in news media @xcite . due to its hazard, pcl is classified as a milder form of toxic speech @xcite . the increasingly social issue caused by pcl has attracted considerable attention of researchers in the natural language processing (nlp) field. wang and potts (2019) introduced the task of condescension detection in direct communication and built a dataset with annotated social media messages. pérez-almendros et al. (2020) proposed don't patronize me!, an annotated dataset with pcl, and demonstrated the effectiveness of the model for pcl detection (kenton and toutanova, 2019). ## emotional polarity and intensity of pcl in this section, we conduct a further analysis to explore the relevance between pcl and emotional polarity and intensity it contains. we employ nltk foot_4 , a natural language processing toolkit, to determine the emotional features of a paragraph. for a given text, parser of nltk returns a sentiment score in a interval of [-1,1], which determines if sample is positive or negative and shows emotional intensity. we divide the sentiment score into 5 levels, and the mapping relationships reflecting motional polarity and intensity are shown in ## conclusion and future work in this work, we present our approach to the semeval-2022 task 4 to tackle the problem of patronizing and condescending language detection. we employ adversarial training and balancing methods for pcl classification with long-tailed class distribution and demonstrate the effectiveness of our methods. besides basic deep learning techniques, introducing multi-task learning in pcl detection, such as predicting the sentiment polarity of a paragraph, is also a problem worth discussing. we have found that pcl is associated with the emotional polarity and intensity of paragraphs. in the future, we will further explore the relationship between sentiment analysis and pcl detection and propose corresponding multitasking frameworks.