Papers by Anton Chernyavskiy
Extract and Aggregate: A Novel Domain-Independent Approach to Factual Data Verification (D19-66)
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| Challenge: | Existing methods to verify information are used to verify factual data . a domain-independent fact checking system can solve the problem entirely or at the individual stages. |
| Approach: | They propose a domain-independent fact checking system that can solve the verification problem entirely or at the individual stages. |
| Outcome: | The proposed model can achieve a score on par with state-of-the-art models based on specific datasets . it can be used to verify the truth or falsity of the fact, the authors say . |
ZenPropaganda: A Comprehensive Study on Identifying Propaganda Techniques in Russian Coronavirus-Related Media (2024.lrec-main)
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| Challenge: | a new classification scheme for automatic detection of propaganda techniques is proposed . the capabilities of algorithms increase the risks of propaganda impact on the audience . |
| Approach: | They propose a novel multi-level classification scheme for automatic detection of propaganda techniques. |
| Outcome: | The proposed classification scheme outperforms existing methods in a Russian dataset and provides a valuable resource for future research. |
CrowdChecked: Detecting Previously Fact-Checked Claims in Social Media (2022.aacl-main)
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| Challenge: | Existing systems to automate fact-checking lack credibility in the eyes of the users. |
| Approach: | They propose to perform automatic fact-checking by verifying whether an input claim has been fact- checked by professional fact- checkers and to return back an article that explains their decision. |
| Outcome: | The proposed method improves on the CLEF’21 CheckThat! test set by two points absolute. |
Batch-Softmax Contrastive Loss for Pairwise Sentence Scoring Tasks (2022.naacl-main)
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| Challenge: | Recent advances in machine learning have led to the use of contrastive loss for representation learning. |
| Approach: | They propose to use batch-softmax contrastive loss to train pairwise sentence embeddings . they propose to take a batch-softermax contrastitive loss and train it with different loss functions . |
| Outcome: | The proposed model improves on a number of datasets and pairwise sentence scoring tasks. |