news S weeper at S em E val-2020 Task 11: Context-Aware Rich Feature Representations for Propaganda Classification (2020)

Abstract: This paper describes our submissions to SemEval 2020 Task 11: Detection of Propaganda Techniques in News Articles for each of the two subtasks of Span Identification and Technique Classification. We make use of pre-trained BERT language model enhanced with tagging techniques developed for the task of Named Entity Recognition (NER), to develop a system for identifying propaganda spans in the text. For the second subtask, we incorporate contextual features in a pre-trained RoBERTa model for the classification of propaganda techniques. We were ranked 5th in the propaganda technique classification subtask.

## introduction propaganda is commonly defined as information of a biased or misleading nature, possibly purposefully shaped, to promote an agenda or a cause. commonly used propaganda techniques are psychological and rhetorical -ranging from the selective presentation of facts and logical fallacies to the use of loaded language to produce an emotional response. historically, propaganda has been widely employed and is often associated with governments, activists, big business, religious organizations, and partisan media. having been first introduced in a previous iteration of the semeval shared task @xcite , the current shared task @xcite ) consists of two subtasks, (i) span identification(si): given a plain-text document, identify those specific fragments which are propagandistic, and (ii) technique classification(tc): given a text fragment identified as propaganda and its document context, identify the applied propaganda technique in the fragment. both the subtasks focus on english texts only. an illustration is shown in figure 1 . the previous iteration of this task required identifying the propaganda spans and also identifying the propaganda technique for that fragment. the highest scoring system by @xcite employed an out-of-the-box bert @xcite language model fine-tuned on the token classification task -whether the token is part of a propaganda span or not. for the classification task, the same system employed an n+2 way classification, where n is the number of propaganda classes. for the technique classification, we make use of document context. for context inclusion, we build upon an approach by @xcite , where they use title and previous sentence as context. they pass the context concatenated with the original text for fine-tuning the bert model. our approach for span identification makes use of a state-of-the-art language model enhanced by tagging schemes inspired from named entity recognition which aim to model spans better -logically and intuitively -by involving begin and end tags @xcite to better formulate spans. in this regard, we experimented with bert, roberta @xcite , spanbert @xcite , gpt2 @xcite language models and with bio, bioe, bioes tagging schemes. for the final technique classification model, we use roberta language model to get the contextual sequence representation for the propaganda fragment and perform classification. our best performing models ranked 13 th for si subtask and 5 th for the tc subtask on an unknown test set. the implementation for our system is made available via github foot_0 . ## problem statement for the si subtask, given a document d we have to identify all such fragments of text which are propagandistic. these character level input spans are converted to token level labels (see §4.2.1), which makes this problem a token classification problem. the approach has been detailed out in § 3.1. for the tc subtask, given a span identified as propagandistic, we have to classify it into one of the 14 techniques @xcite . note that we are also given the document d containing the span which we can use as context. the approach has been detailed out in § 3.2. in § 4.1 we describe the dataset. ## conclusion we proposed models for detecting propaganda fragments in articles and classifying the propaganda technique used in a given propaganda fragment. we portray how state-of-the-art language models can be useful for both subtasks. we show how bioe tagging scheme can help detect spans better. for classification, we model a way to include context surrounding a propaganda fragment, although its inclusion does not improve the predictions significantly. finally, we also show how the span lengths are distributed for different categories. modeling this fact by doing hierarchical classification is something that can be explored in the future.