aschern at SemEval-2020 Task 11: It Takes Three to Tango: RoBERTa, CRF, and Transfer Learning

TL;DR

This paper presents an ensemble system using RoBERTa, CRF, and transfer learning for detecting propaganda techniques in news articles, achieving top-tier results in SemEval-2020 Task 11.

Contribution

It introduces a novel ensemble approach combining RoBERTa, CRF, and transfer learning, with advanced post-processing for multi-label span detection.

Findings

Ranked 3rd in span identification with 0.491 F1 score

Achieved 2nd in technique classification with 0.62 F1 score

Significant improvement over baseline models

Abstract

We describe our system for SemEval-2020 Task 11 on Detection of Propaganda Techniques in News Articles. We developed ensemble models using RoBERTa-based neural architectures, additional CRF layers, transfer learning between the two subtasks, and advanced post-processing to handle the multi-label nature of the task, the consistency between nested spans, repetitions, and labels from similar spans in training. We achieved sizable improvements over baseline fine-tuned RoBERTa models, and the official evaluation ranked our system 3rd (almost tied with the 2nd) out of 36 teams on the span identification subtask with an F1 score of 0.491, and 2nd (almost tied with the 1st) out of 31 teams on the technique classification subtask with an F1 score of 0.62.