# WVOQ at SemEval-2021 Task 6: BART for Span Detection and Classification

arXiv: https://arxiv.org/abs/2107.05467 · HTML: https://mlsanity.com/paper/2107.05467/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 6 references that are in the map.
- First posted on arXiv 27 Jun 2021.

## Builds on (most cited first, 5 of 5)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019)
- [Prta: A System to Support the Analysis of Propaganda Techniques in the News](https://mlsanity.com/paper/2005.05854/) (2020)
- [aschern at SemEval-2020 Task 11: It Takes Three to Tango: RoBERTa, CRF, and Transfer Learning](https://mlsanity.com/paper/2008.02837/) (2020)

## Built on by (most cited first, 1 of 1)

- [Call for Rigor in Reporting Quality of Instruction Tuning Data](https://mlsanity.com/paper/2503.04807/) (2025)

## 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.