English

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

Computation and Language 2021-07-13 v1 Machine Learning

Abstract

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.

Keywords

Cite

@article{arxiv.2107.05467,
  title  = {WVOQ at SemEval-2021 Task 6: BART for Span Detection and Classification},
  author = {Cees Roele},
  journal= {arXiv preprint arXiv:2107.05467},
  year   = {2021}
}

Comments

5 pages, 1 figure, accepted at SemEval-2021 co-located with ACL-IJCNLP 2021

R2 v1 2026-06-24T04:06:30.938Z