NoPropaganda at SemEval-2020 Task 11: A Borrowed Approach to Sequence Tagging and Text Classification

Proceedings of the 14th International Workshop on Semantic Evaluation, pages 1488–1494 Barcelona, Spain (Online), December 12, 2020. 1488 NoPropaganda at SemEval-2020 Task 11: A Borrowed Approach to Sequence Tagging and Text Classification Ilya Dimov ABBYY ARD NLP Lomonosov Moscow State University ilia.dimov@abbyy.com Vladislav Korzun ABBYY ARD NLP MIPT ABBYY Lab vladislav.korzun@abbyy.com Ivan Smurov ABBYY ARD NLP MIPT ABBYY Lab ivan.smurov@abbyy.com Abstract This paper describes our contribution to SemEval-2020 Task 11: Detection Of Propaganda Techniques In News Articles. We start with simple LSTM baselines and move to an autoregressive transformer decoder to predict long continuous propaganda spans for the first subtask. We also adopt an approach from relation extraction by enveloping spans mentioned above with special tokens for the second subtask of propaganda technique classification. Our models report an F-score of 44.6% and a micro-averaged F-score of 58.2% for those tasks accordingly. 1 Introduction In recent years natural language processing has experienced rapid development. In particular, application of deep learning to NLP (Collobert et al., 2011), the introduction of pre-trained context-independent word embeddings such as word2vec (Mikolov et al., 2013a; Mikolov et al., 2013b), GloVe (Pennington et al., 2014) and fastText (Bojanowski et al., 2017), and utilization of RNN-based (Hochreiter and Schmidhuber, 1997) pipelines such as CharCNN-BLSTM-CRF (Lample et al., 2016; Ma and Hovy, 2016) allowed to improve SOTA for an overwhelming majority of NLP tasks. In 2018 a monumental breakthrough happened when context-dependent embeddings based on pre-trained language models emerged, such as ELMo (Peters et al., 2018), ULMFit (Howard and Ruder, 2018), and BERT (Devlin et al., 2018). The resulting boost in the models’ performance cannot be underestimated, amounting to a 30% relative error reduction rate. Researchers compare this latest development to the introduction of ImageNet to Computer Vision. Furthermore, as breakthroughs in CV allowed such technologies as DeepFake to arise, similar processes are happening to NLP: the authors of GPT2 (Radford et al., 2019) anticipated that their model could be used for malicious purposes such as fake news generation or even impersonation. Given that at present, the problem of media swaying public opinion to the benefit of certain parties is more acute than ever, we must keep our news fair and unbiased; thus, techniques for fake news and propaganda detection need to be developed. SemEval-2020 task 11 is an essential step in that direction. Another noteworthy consequence of contextualized embeddings introduction is the generalization of models. While many models are developed for a particular task, they can be effectively utilized in order to solve other tasks. Our main contribution is the utilization of two such models for new tasks, namely LaserTagger (Malmi et al., 2019), initially developed for summarization, which is used for span identification, while R-BERT (Wu and He, 2019), originally developed for relation extraction, is used for span classification. Section 2 contains the description of our systems1, section 3 contains evaluation results and error analysis, while part 4 contains the concluding remark. 1The code is released at https://github.com/hawkeoni/Semeval2020_task11. This work is licensed under a Creative Commons Attribution …

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