Papers by Ashkan Kazemi
Claim Matching Beyond English to Scale Global Fact-Checking (2021.acl-long)
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| Challenge: | Existing methods to fact-check content are not scaled well in non-English contexts. |
| Approach: | They propose to use a WhatsApp tipline and public group message dataset to find pairs of textual messages containing claims that can be served with one fact-check. |
| Outcome: | The proposed model outperforms existing models in English, Hindi, and Tamil in all settings. |
Biased TextRank: Unsupervised Graph-Based Content Extraction (2020.coling-main)
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| Challenge: | TextRank ranks text spans according to their importance for language processing tasks and their relevance to an input “focus.” |
| Approach: | They propose a graph-based content extraction method inspired by TextRank that ranks text spans according to their importance for language processing tasks and according to relevance to an input “focus.” |
| Outcome: | The proposed method improves on two different datasets by significant ROUGE-N score margins. |
Has It All Been Solved? Open NLP Research Questions Not Solved by Large Language Models (2024.lrec-main)
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Oana Ignat, Zhijing Jin, Artem Abzaliev, Laura Biester, Santiago Castro, Naihao Deng, Xinyi Gao, Aylin Ece Gunal, Jacky He, Ashkan Kazemi, Muhammad Khalifa, Namho Koh, Andrew Lee, Siyang Liu, Do June Min, Shinka Mori, Joan C. Nwatu, Veronica Perez-Rosas, Siqi Shen, Zekun Wang, Winston Wu, Rada Mihalcea
| Challenge: | Recent advances in large language models have led to misleading public discourse that “it’s all been solved.” |
| Approach: | They identify 14 research areas encompassing 45 research directions that require new research and are not directly solvable by LLMs. |
| Outcome: | The research areas identified are 45 research directions that require new research and are not directly solvable by LLMs. |
When Claims Evolve: Evaluating and Enhancing the Robustness of Embedding Models Against Misinformation Edits (2025.findings-acl)
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| Challenge: | Existing claims-matching systems that use sentence embedding models are not robust to edits as users interact with claims online. |
| Approach: | They propose a perturbation framework that generates valid and natural claim variations and evaluate different mitigation approaches to improve their findings. |
| Outcome: | The proposed framework evaluates embedding models in a multi-stage retrieval pipeline and identifies the effectiveness of mitigation approaches. |