Papers by Tariq Alhindi
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. |
DeSePtion: Dual Sequence Prediction and Adversarial Examples for Improved Fact-Checking (2020.acl-main)
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Christopher Hidey, Tuhin Chakrabarty, Tariq Alhindi, Siddharth Varia, Kriste Krstovski, Mona Diab, Smaranda Muresan
| Challenge: | Fact Extraction and Verification datasets provide a resource for end-to-end fact-checking, requiring retrieval of evidence from Wikipedia to validate a veracity prediction. |
| Approach: | They propose a system that is resilient to attacks by multiple propositions, temporal reasoning, ambiguity and lexical variation and a sequence of evidence sentences and veracity relation predictions. |
| Outcome: | The proposed system is resilient to three realistic “attacks” and obtains state-of-the-art results due to improved evidence retrieval. |
Multitask Instruction-based Prompting for Fallacy Recognition (2022.emnlp-main)
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| Challenge: | Fallacies are used as seemingly valid arguments to support a position and persuade the audience about its validity. |
| Approach: | They propose to use instruction-based prompting to recognize 28 unique fallacies across datasets . they also analyze the effect of model size and prompt choice on model performance . |
| Outcome: | The proposed approach can recognize 28 unique fallacies across domains and genres. |
Fact vs. Opinion: the Role of Argumentation Features in News Classification (2020.coling-main)
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| Challenge: | A 2018 study led by the Media Insight Project showed that most journalists think that their news organizations should clearly mark what is news reporting and what is commentary or opinion in order to combat fake news and gain public trust. |
| Approach: | They propose to classify news articles into newsstories and opinion pieces using models that aim to sup-plement the article content representation with argumentation features. |
| Outcome: | The proposed model outperforms linguistic features and improves on fine-tuned transformer-based models on data from publishers. |
Large Language Models are Few-Shot Training Example Generators: A Case Study in Fallacy Recognition (2024.findings-acl)
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| Challenge: | Existing work on fallacy recognition is still in its early stages, with limited datasets available. |
| Approach: | They propose to use GPT3.5 to generate synthetic examples and explore prompt settings to improve the representation of the infrequent classes. |
| Outcome: | The proposed model improves on existing models and generates synthetic examples with GPT3.5. |