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 . |
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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. |
Fine-Grained Propaganda Detection with Fine-Tuned BERT (D19-50)
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| 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. |
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. |
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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. |
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NSIT@NLP4IF-2019: Propaganda Detection from News Articles using Transfer Learning (D19-50)
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| Challenge: | In this paper, we describe our approach and system description for NLP4IF 2019 Workshop: Shared Task on Fine-Grained Propaganda Detection. |
| Approach: | They propose to use document Embeddings and LSTM to detect whether a sentence contains a propagandistic agenda. |
| Outcome: | The proposed approach ranked 21st in the NLP4IF 2019 Workshop: Shared Task on Fine-Grained Propaganda Detection. |
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 . |
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. |
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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. |
On Sentence Representations for Propaganda Detection: From Handcrafted Features to Word Embeddings (D19-50)
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| Challenge: | The rise of fake and hyperpartisan news on social media and online news outlets calls for improved automatic detection of propaganda in texts. |
| Approach: | They propose to use handcrafted features and learn dense semantic representations to detect propaganda in sentence-level and with random undersampling of the majority class (non-propaganda) |
| Outcome: | The proposed system achieves a ranking of 10 among 25 participants, with 59.5 F1-score. |
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. |