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Taisiya Glushkova

2 accepted papers

2022

Disentangling Uncertainty in Machine Translation Evaluation

EMNLP 2022main

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. Recent work has attempted to mitigate this with simple uncertainty quantification te…

2021

Uncertainty-Aware Machine Translation Evaluation

EMNLP 2021finding

Several neural-based metrics have been recently proposed to evaluate machine translation quality. However, all of them resort to point estimates, which provide limited information at segment level. This is made worse as they are trained on noisy, biased and scarce human judgements, often resulting i…