R e DASP ersuasion at S em E val-2023 Task 3: Persuasion Detection using Multilingual Transformers and Language Agnostic Features (2023)

Abstract: This paper describes a multilingual persuasion detection system that incorporates persuasion technique attributes for a multi-label classification task. The proposed method has two advantages. First, it combines persuasion features with a sequence classification transformer to classify persuasion techniques. Second, it is a language agnostic approach that supports a total of 100 languages, guaranteed by the multilingual transformer module and the Google translator interface. We found that our persuasion system outperformed the SemEval baseline in all languages except zero shot prediction languages, which did not constitute the main focus of our research. With the highest F1-Micro score of 0.45, Italian achieved the eighth position on the leaderboard.

## introduction the goal of the semeval-2023 task 3 @xcite subtask 3 is to detect the presence of one or many persuasion techniques in text data. it is a multilingual task and consisted initially of six languages: english, french, polish, italian, russian and german. in the testing phase, organizers added three surprise languages (spanish, greek, and georgian) for zero-shot classification tasks. in this work, we aim to build a cross-lingual predictive model that detects persuasion techniques in a given text with the help of language transformers and linguistic features. our system uses xlm-roberta @xcite as a backbone model, incorporating language agnostic features provided using google translation over 100 languages for persuasion detection. xlm-roberta has been shown to be a powerful multilingual pretrained language model compared against other models like multilingual bert (m-bert) @xcite and distilbert @xcite . also, this language transformer can process all the languages existing in the semeval-2023 task3. through this task, we realized the importance of data quality in multi-label classification tasks with cross-lingual settings. we were also able to build a new system that incorporates language agnostic features to capture persuasion in text semantics, accompanied by the powerful capacity of language transformers based on attention mechanism. ## system overview in this section, we describe our model and the steps in our approach. figure 1 depicts the architecture of the entire redaspersuasion system. our system consists of three modules: (i) the first module is a multilingual transformer model, (ii) the second feature engineering module focuses ## experimental results we ran all classification experiments on a high performing cluster machine with an intel® xeon® gold 6252 (3.70ghz) processor with 24 cores and 48 threads running linux red hat enterprise server 8.6 (with nvidia® volta v100 gpu for our pytorch-lightning foot_5 implemented system). on average, it took approximately 31 minutes and 29 seconds to train, validate and predict data across all six languages (more details in in this section, we will provide all experimental results starting by semeval task-3 baseline. in accordance with the task organizers' request, all fine-tuning and enhancements made to our system after the deadline have been included in section 4.3 ## conclusions and future work the model's performance has continually improved since the leaderboard reopened. from recent results, we found that performance improvement is evident as the model size grows (from xlm-r base to large) but also that tuning the model can immensely enhance the model's training and ability to generalize data correctly. a future direction would be to create an instance of our method to include zero-shot classification tasks using transfer learning. in fact, the transformer has the ability to be trained on zero-shot data, using additional features which can be persuasion attributes in our system. we would like to further explore the persuasive text interpretability per class to better understand the language indicators of persuasion in text.