SSN _ NLP _ MLRG at S em E val-2022 Task 4: Ensemble Learning strategies to detect Patronizing and Condescending Language (2022)

Abstract: In this paper, we describe our efforts at SemEval 2022 Shared Task 4 on Patronizing and Condescending Language (PCL) Detection. This is the first shared task to detect PCL which is to identify and categorize PCL language towards vulnerable communities. The shared task consists of two subtasks: Patronizing and Condescending language detection (Subtask A) which is the binary task classification and identifying the PCL categories that express the condescension (Subtask B) which is the multi-label text classification. For PCL language detection, We proposed the ensemble strategies of a system combination of BERT, Roberta, Distilbert, Roberta large, Albert achieved the official results for Subtask A with a macro f1 score of 0.5172 on the test set which is improved by baseline score. For PCL Category identification, We proposed a multi-label classification model to ensemble the various Bert-based models and the official results for Subtask B with a macro f1 score of 0.2117 on the test set which is improved by baseline score.

## introduction social media is a wide platform and it grows rapidly. people can communicate with each other and express their opinions easily without any hesitation on social media. patronizing and condescending language (pcl) 1 is language use shows a superior attitude rise towards vulnerable communities in the social media. this effect is unconscious and the intention is trying to help communities like an individual, group of people in need by raising awareness, moving the user audience to action, and standing for the rights. however, these dominant attitudes can lead to discrimination and the user audience is unaware of this diminishing treatment due to its subtlety. moreover, online social media publications reached more audiences in day-to-day life and we noticed that diminishing treatment of 1 https://www.merriam-webster.com/dictionary/patronizing vulnerable groups leads to greater inequalities. so, pcl can potentially be very harmful, as it feeds stereotypes, routinizes discrimination, and drives to greater exclusion. detecting pcl language and its categorization of pcl language on social media have gained a lot of interest recently. the detection of pcl is still an emergent area of study in nlp. to the best of my knowledge, this is the first shared task foot_0 to detect the pcl and their categories from the vulnerable communities. the challenge is to detect that pcl is difficult both for humans and nlp systems, due to its subtle nature, and its subjectivity reasoning required to understand this kind of language. se-meval 2022 task 4 presents the problems of detecting pcl and its categorizes of pcl which express the condescending language in english tweets to the nlp community. the pcl shared task consists of two subtasks: subtask a is to identify the content is pcl or not pcl. subtask b is to classify whether the content into unbalanced power relations, shallow solution, presupposition, authority voice, metaphor, compassion, the poorer, the merrier. this paper describes the systems submitted for semeval 2022 shared task on pcl detection by the team ssn_nlp_mlrg. we have participated in the shared task for all the two subtasks. first, we experimented with bert-based models and we used the ensembling strategies to enhance the performance of the model. finally, we performed voting to decide the final output. the majority voting on 5 classification models yielded better results than individual systems. the paper is organized as follows: in section 2, we describe the background work, in section 3, we describe our models, we present the experimental setup in section 4, and compare results in section 5, we provide the conclusion of our work. ## background in this section, we describe the task provided to the participants and the two subtasks. ## methodology we used the pre-trained models bert (bidirectional encoder representations from transformers). we fine-tune a bert language model @xcite for pcl classification. we also finetuned a roberta-base (a robustly optimized bert pretraining approach) model @xcite to classify pcl language and the pcl categories which can be expressed condescension and viewed as an optimized version of bert. we used two variants of the roberta method that are robertalarge-cased and roberta-base-cased pre-trained models. we also fine-tune the distilbert (distilled version of bert model) model @xcite is the transformer model, which is a lighter and faster variant of bert. we used the albert (a lite bert) model @xcite to fine-tune the system to predict the pcl language. to further explore the performance, we apply the ensemble strategies to combine the transformers models output to predict the pcl and category of pcl text subtask a subtask b 1. the scheme saw an estimated 150,000 children from poor families being sent to parts of the british empire between 1920 and 1974 , by religious orders and charities who said they would lead better lives 1 [1, 0, 0, 1, 0, 0, 0] 2. durban 's homeless communities reconciliation lunch 0 [0, 1, 0, 0, 0, 0, 0] ## conclusion and future work this paper presents the submitted runs for the patronizing and condensing language identification in semeval 2022 task 4. the results show that the not-pcl language and pcl language in the dataset receives the same macro f1 scores. we experimented with different approaches such as a bert model, albert, roberta base, roberta large, and distilbert and ensemble models. based on the evaluation, bert performs well for subtask b to classify the pcl content into seven categories that express condensing language. ensemble model performs well for subtask a to detect the content is pcl language or not pcl language. our team submission had a macro f1 score of 0.5172 for subtask a and a macro f1 score of 0.2117 for subtask b which are improved by the baseline f1 scores. for future work, we will handle the imbalanced dataset by using external resources and apply the data augmentation techniques to enhance the performance of our model.