Cost-Sensitive BERT for Generalisable Sentence Classification on Imbalanced Data (D19-50)
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| Challenge: | Popular NLP tasks such as sentiment analysis and event extraction from social media are examples of imbalanced classification problems. |
| Approach: | They propose a method to generalise on dissimilar training and test data using a measure of similarity between datasets. |
| Outcome: | The proposed method achieves the second highest score on sentence-level propaganda classification. |
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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. |
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. |
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 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. |
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 . |
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. |
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. |
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. |
Fusing Label Embedding into BERT: An Efficient Improvement for Text Classification (2021.findings-acl)
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| Challenge: | Existing methods to improve text classification performance of pre-trained models have been used to improve their performance. |
| Approach: | They propose a method for improving BERT's performance by using a label embedding technique while keeping almost the same computational cost. |
| Outcome: | The proposed method improves BERT's performance on six text classification benchmark datasets while keeping almost the same computational cost. |