Team DoNotDistribute at SemEval-2020 Task 11: Features, Finetuning, and
Data Augmentation in Neural Models for Propaganda Detection in News Articles
- URL: http://arxiv.org/abs/2008.09703v1
- Date: Fri, 21 Aug 2020 22:35:57 GMT
- Title: Team DoNotDistribute at SemEval-2020 Task 11: Features, Finetuning, and
Data Augmentation in Neural Models for Propaganda Detection in News Articles
- Authors: Michael Kranzlein, Shabnam Behzad, Nazli Goharian
- Abstract summary: This paper presents our systems for SemEval 2020 Shared Task 11: Detection of Propaganda Techniques in News Articles.
We participate in both the span identification and technique classification subtasks and report on experiments using different BERT-based models along with handcrafted features.
- Score: 13.339333273943843
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper presents our systems for SemEval 2020 Shared Task 11: Detection of
Propaganda Techniques in News Articles. We participate in both the span
identification and technique classification subtasks and report on experiments
using different BERT-based models along with handcrafted features. Our models
perform well above the baselines for both tasks, and we contribute ablation
studies and discussion of our results to dissect the effectiveness of different
features and techniques with the goal of aiding future studies in propaganda
detection.
Related papers
- SmurfCat at SemEval-2024 Task 6: Leveraging Synthetic Data for Hallucination Detection [51.99159169107426]
We present our novel systems developed for the SemEval-2024 hallucination detection task.
Our investigation spans a range of strategies to compare model predictions with reference standards.
We introduce three distinct methods that exhibit strong performance metrics.
arXiv Detail & Related papers (2024-04-09T09:03:44Z) - Harnessing Diffusion Models for Visual Perception with Meta Prompts [68.78938846041767]
We propose a simple yet effective scheme to harness a diffusion model for visual perception tasks.
We introduce learnable embeddings (meta prompts) to the pre-trained diffusion models to extract proper features for perception.
Our approach achieves new performance records in depth estimation tasks on NYU depth V2 and KITTI, and in semantic segmentation task on CityScapes.
arXiv Detail & Related papers (2023-12-22T14:40:55Z) - RoBLEURT Submission for the WMT2021 Metrics Task [72.26898579202076]
We present our submission to the Shared Metrics Task: RoBLEURT.
Our model reaches state-of-the-art correlations with the WMT 2020 human annotations upon 8 out of 10 to-English language pairs.
arXiv Detail & Related papers (2022-04-28T08:49:40Z) - Guiding Generative Language Models for Data Augmentation in Few-Shot
Text Classification [59.698811329287174]
We leverage GPT-2 for generating artificial training instances in order to improve classification performance.
Our results show that fine-tuning GPT-2 in a handful of label instances leads to consistent classification improvements.
arXiv Detail & Related papers (2021-11-17T12:10:03Z) - DAGA: Data Augmentation with a Generation Approach for Low-resource
Tagging Tasks [88.62288327934499]
We propose a novel augmentation method with language models trained on the linearized labeled sentences.
Our method is applicable to both supervised and semi-supervised settings.
arXiv Detail & Related papers (2020-11-03T07:49:15Z) - Solomon at SemEval-2020 Task 11: Ensemble Architecture for Fine-Tuned
Propaganda Detection in News Articles [0.3232625980782302]
This paper describes our system (Solomon) details and results of participation in the SemEval 2020 Task 11 "Detection of Propaganda Techniques in News Articles"
We used RoBERTa based transformer architecture for fine-tuning on the propaganda dataset.
Compared to the other participating systems, our submission is ranked 4th on the leaderboard.
arXiv Detail & Related papers (2020-09-16T05:00:40Z) - SemEval-2020 Task 11: Detection of Propaganda Techniques in News
Articles [0.6999740786886536]
We present the results of SemEval-2020 Task 11 on Detection of Propaganda Techniques in News Articles.
The task featured two subtasks: Span Identification and Technique Classification.
For both subtasks, the best systems used pre-trained Transformers and ensembles.
arXiv Detail & Related papers (2020-09-06T10:05:43Z) - DUTH at SemEval-2020 Task 11: BERT with Entity Mapping for Propaganda
Classification [1.5469452301122173]
This report describes the methods employed by the Democritus University of Thrace (DUTH) team for participating in SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles.
arXiv Detail & Related papers (2020-08-22T18:18:02Z) - LTIatCMU at SemEval-2020 Task 11: Incorporating Multi-Level Features for
Multi-Granular Propaganda Span Identification [70.1903083747775]
This paper describes our submission for the task of Propaganda Span Identification in news articles.
We introduce a BERT-BiLSTM based span-level propaganda classification model that identifies which token spans within the sentence are indicative of propaganda.
arXiv Detail & Related papers (2020-08-11T16:14:47Z) - newsSweeper at SemEval-2020 Task 11: Context-Aware Rich Feature
Representations For Propaganda Classification [2.0491741153610334]
This paper describes our submissions to SemEval 2020 Task 11: Detection of Propaganda Techniques in News Articles.
We make use of pre-trained BERT language model enhanced with tagging techniques developed for the task of Named Entity Recognition.
For the second subtask, we incorporate contextual features in a pre-trained RoBERTa model for the classification of propaganda techniques.
arXiv Detail & Related papers (2020-07-21T14:06:59Z) - BPGC at SemEval-2020 Task 11: Propaganda Detection in News Articles with
Multi-Granularity Knowledge Sharing and Linguistic Features based Ensemble
Learning [2.8913142991383114]
SemEval 2020 Task-11 aims to design automated systems for news propaganda detection.
Task-11 consists of two sub-tasks, namely, Span Identification and Technique Classification.
arXiv Detail & Related papers (2020-05-31T19:35:53Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.