Papers by Sofia Serrano
Stubborn Lexical Bias in Data and Models (2023.findings-acl)
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| Challenge: | Recent work has focused on spurious correlations between features and labels in training data . but, we find strong evidence of corresponding bias in the trained models . |
| Approach: | They propose a method to reduce spurious correlations in training data by reweighting it using a large pool of extracted features. |
| Outcome: | The proposed method reduces spurious correlations in training data, but still finds strong evidence of bias in trained models. |
Is Attention Interpretable? (P19-1)
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| Challenge: | Attention mechanisms have recently boosted performance on a range of NLP tasks. |
| Approach: | They propose to manipulate attention weights in text classification models and analyze the resulting differences in their predictions. |
| Outcome: | The proposed approach improves models' predictions by using gradient-based rankings of attention weights. |
All That’s ‘Human’ Is Not Gold: Evaluating Human Evaluation of Generated Text (2021.acl-long)
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| Challenge: | evaluators distinguish between human- and machine-authored text in three domains without training . evals' accuracy improved up to 55%, but it did not significantly improve across the three domain. |
| Approach: | They examine the role untrained human evaluations play in NLG evaluation and propose ways to improve their evaluations. |
| Outcome: | The evaluators distinguished between human- and machine-authored text at random chance level without training, but their accuracy did not improve across the three domains. |