WVOQ at SemEval-2021 Task 6: BART for Span Detection and Classification
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Abstract (arXiv)
A novel solution to span detection and classification is presented in which a BART EncoderDecoder model is used to transform textual input into a version with XML-like marked up spans. This markup is subsequently translated to an identification of the beginning and end of fragments and of their classes. Discussed is how pre-training methodology both explains the relative success of this method and its limitations. This paper reports on participation in task 6 of SemEval-2021: Detection of Persuasion Techniques in Texts and Images.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 5 of its 6 references that are in the map. First posted on arXiv 27 Jun 2021.
Builds on
5 of 6 references.
References · 5
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
- 2019SpanBERT: Improving Pre-training by Representing and Predicting Spanscited by 832
- 2020Prta: A System to Support the Analysis of Propaganda Techniques in the Newscited by 23
- 2020aschern at SemEval-2020 Task 11: It Takes Three to Tango: RoBERTa, CRF, and Transfer Learningcited by 6
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