Papers by Ieva Staliunaite
Dis2Dis: Explaining Ambiguity in Fact-Checking (2025.findings-naacl)
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| Challenge: | Ambiguity is a linguistic tool for encoding information efficiently, yet it also causes misunderstandings and disagreements. |
| Approach: | They propose a constrained generation task for explaining ambiguous claims in fact-checking by editing them to spell out an interpretation that can be unequivocally supported by the given evidence. |
| Outcome: | The proposed model disambiguates claims 72% of the time compared to a simple copy baseline and a Large Language Model baseline. |
Uncertainty Quantification for Evaluating Gender Bias in Machine Translation (2026.findings-eacl)
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| Challenge: | Existing models can reproduce existing social inequalities but cannot be reduced. |
| Approach: | They propose that models should maintain uncertainty when input is ambiguous to avoid reinforcing biases. |
| Outcome: | The proposed model can detect gender bias when translated to ambiguous and unambiguous sources and shows that it does not correlate with high translation accuracy and debiases the two cases differently. |