Papers by Taisiya Glushkova
Uncertainty-Aware Machine Translation Evaluation (2021.findings-emnlp)
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| Challenge: | Several neural-based metrics have been proposed to evaluate machine translation quality, but they are trained on noisy, biased and scarce human judgements. |
| Approach: | They propose a method to evaluate machine translation quality using point estimates . they combine COMET framework with Monte Carlo dropout and deep ensembles . |
| Outcome: | The proposed methods perform well across multiple language pairs and with references. |
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. |