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cs.CL2021
Understanding Model Robustness to User-generated Noisy Texts
Jakub Náplava, Martin Popel, Milan Straka +1
Sensitivity of deep-neural models to input noise is known to be a challenging problem. In NLP, model performance often deteriorates with naturally occurring noise, such as spelling…
cs.CL2021
Neural Machine Translation Quality and Post-Editing Performance
Vilém Zouhar, Aleš Tamchyna, Martin Popel +1
We test the natural expectation that using MT in professional translation saves human processing time. The last such study was carried out by Sanchez-Torron and Koehn (2016) with p…