Papers by Tanya Roosta
ClaimVer: Explainable Claim-Level Verification and Evidence Attribution of Text Through Knowledge Graphs (2024.findings-emnlp)
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Preetam Prabhu Srikar Dammu, Himanshu Naidu, Mouly Dewan, YoungMin Kim, Tanya Roosta, Aman Chadha, Chirag Shah
| Challenge: | Despite the fact that many fact-checking tools lack granularity and explainability, they lack the ability to be useful in various contexts. |
| Approach: | They propose a text validation framework that provides granular explanations for each claim and localizes the specific problematic content to reduce cognitive load. |
| Outcome: | The proposed framework provides granular explanations for each claim prediction and localizes and educates users on the specific content. |
Training Mixed-Domain Translation Models via Federated Learning (2022.naacl-main)
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| Challenge: | Experimental results show that neural machine translation engines built via FL can be easily adapted when an FL-based aggregation is applied to fuse different domains. |
| Approach: | They propose to use federated learning to fuse mixed-domain translation models with a centralized aggregation to improve their performance. |
| Outcome: | The proposed model can be easily adapted to a mixed-domain translation model with slight modifications in the training process and perform on par with state-of-the-art training models. |