| Challenge: | The goal of the Fragment Level Classification task is to detect and classify textual segments that correspond to one of the 18 given propaganda techniques in a news articles dataset. |
| Approach: | They propose a model that performs word-level classification using a pre-trained language model to detect and classify propaganda fragments in a news article dataset. |
| Outcome: | The proposed model performs word-level classification using a popular pre-trained language model. |
Similar Papers
Fine-Tuned Neural Models for Propaganda Detection at the Sentence and Fragment levels (D19-50)
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| Challenge: | The system was evaluated on a unified development set without distributing the gold labels. |
| Approach: | They propose to use fine-grained propaganda detection to build models that can explain why an article is propagandistic. |
| Outcome: | The proposed model performed on all eighteen propaganda techniques in the corpus of the shared task. |
Findings of the NLP4IF-2019 Shared Task on Fine-Grained Propaganda Detection (D19-50)
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| Challenge: | A shared task on fine-grained propaganda detection was organized at EMNLP-IJCNLP 2019 . 12 systems submitted systems for the FLC task, 25 for the SLC task, and 14 teams submitted a system description paper . |
| Approach: | They present a task on fine-grained propaganda detection as part of the NLP4IF workshop at EMNLP-IJCNLP 2019 . they used a corpus of news articles annotated with an inventory of propagandist techniques at the fragment level to determine the propaganda technique used in each fragment . |
| Outcome: | The shared task on fine-grained propaganda detection was organized at the EMNLP-IJCNLP 2019 . 12 systems submitted for the FLC task, 25 for the SLC task, and 14 submitted a system description paper . |
CAUnLP at NLP4IF 2019 Shared Task: Context-Dependent BERT for Sentence-Level Propaganda Detection (D19-50)
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| Challenge: | Sentence-level and fragment-level propaganda detection tasks are more challenging compared to document-level detection. |
| Approach: | They propose to use context-dependent input pairs to fine-tune the pretrained propaganda detection BERT to better utilize document information. |
| Outcome: | The proposed system can detect propaganda on document-level, sentence-level and fragment-level. |
Pretrained Ensemble Learning for Fine-Grained Propaganda Detection (D19-50)
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| Challenge: | Propaganda detection is a reallife challenge that can affect how people understand news . |
| Approach: | They propose to use a manually annotated dataset to tackle the propaganda detection on sentence level classification task of NLP4IF 2019 workshop co-located with EMNLP-IJCNLP 2019 conference. |
| Outcome: | The proposed model is ranked in the first place with 68.8312 F1-score on the development dataset and in the sixth place with 61.3990 F1 score on the testing dataset. |
Fine-Grained Analysis of Propaganda in News Article (D19-1)
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| Challenge: | Existing methods for detecting propaganda are noisy and lack of explainability. |
| Approach: | They propose to perform fine-grained analysis of texts by detecting all fragments that contain propaganda techniques as well as their type. |
| Outcome: | The proposed model outperforms several strong BERT-based baselines. |
Understanding BERT performance in propaganda analysis (D19-50)
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| Challenge: | Despite the challenging nature of the shared task, our pretrained BERT model scored 0.62 F1 on the test set and ranked third among 25 teams who participated in the contest. |
| Approach: | They propose to use a dataset to fine-tune a model for propaganda analysis at sentence level to determine whether a text is 'propaganda' and to examine false-positive cases. |
| Outcome: | The proposed model scored 0.62 F1 on the test set and ranked third among 25 teams who participated in the shared task. |
Neural Architectures for Fine-Grained Propaganda Detection in News (D19-50)
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| Challenge: | MIC-CIS is a fine grained propaganda detection system . previous work focused on document level, labeling articles as propaganda . |
| Approach: | They propose to use different neural architectures to jointly perform propaganda detection tasks . they also investigate different ensemble schemes such as majority-voting, relax-vote, etc. |
| Outcome: | The proposed system performs sentences and fragment level propaganda detection tasks. |
Sentence-Level Propaganda Detection in News Articles with Transfer Learning and BERT-BiLSTM-Capsule Model (D19-50)
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| Challenge: | a new task is needed to detect propaganda in news articles . the need for communication has increased in online social media platforms . a proposed solution to the problem of sentence-level propaganda classification is ranked 12th . |
| Approach: | They propose to build a binary classifier able to provide corresponding propaganda labels . their solution ranks 12th among 26 teams in the NLP4IF-2019 Shared Task SLC . |
| Outcome: | The proposed model outperforms baseline approach and the winning system on a similar task. |
Divisive Language and Propaganda Detection using Multi-head Attention Transformers with Deep Learning BERT-based Language Models for Binary Classification (D19-50)
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| Challenge: | a team of researchers used a pre-trained BERT language model to train propaganda . the model was based on a cloze comprehension test to answer a question about influence operations . |
| Approach: | team used a BERT language model that was pre-trained on Wikipedia and BookCorpus . they used cloze comprehension tests to train the model to answer a propaganda question . |
| Outcome: | The proposed model was trained on Wikipedia and BookCorpus to answer propaganda questions . the team used a neural network that was pre-trained on the Wikipedia and bookCorpus corpus . |
JUSTDeep at NLP4IF 2019 Task 1: Propaganda Detection using Ensemble Deep Learning Models (D19-50)
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| Challenge: | Detecting fake news is not well established yet, but it can be classified under several labels: false, biased, or framed to mislead the readers. |
| Approach: | They propose a deep learning model using BiLSTM, XGBoost, and BERT to detect propaganda using a corpus from a challenge. |
| Outcome: | The proposed model outperforms the baseline model on a dataset from the challenge NLP4IF 2019 . |