Papers with uncertainty prediction
Disentangling Uncertainty in Machine Translation Evaluation (2022.emnlp-main)
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| Challenge: | Trainable evaluation metrics for machine translation (MT) exhibit strong correlation with human judgements, but they are often hard to interpret and might produce unreliable scores under noisy or out-of-domain data. |
| Approach: | They propose to use Monte Carlo dropout and deep ensembles to quantify uncertainty in machine translation and assess their ability to target different sources of aleatoric and epistemic uncertainty. |
| Outcome: | The proposed measures can target different sources of aleatoric and epistemic uncertainty, with a reduction in computational costs. |