Search Results for author: Lisa Yankovskaya

Found 6 papers, 3 papers with code

BERGAMOT-LATTE Submissions for the WMT20 Quality Estimation Shared Task

no code implementations WMT (EMNLP) 2020 Marina Fomicheva, Shuo Sun, Lisa Yankovskaya, Frédéric Blain, Vishrav Chaudhary, Mark Fishel, Francisco Guzmán, Lucia Specia

We explore (a) a black-box approach to QE based on pre-trained representations; and (b) glass-box approaches that leverage various indicators that can be extracted from the neural MT systems.

Backtranslation Feedback Improves User Confidence in MT, Not Quality

1 code implementation NAACL 2021 Vilém Zouhar, Michal Novák, Matúš Žilinec, Ondřej Bojar, Mateo Obregón, Robin L. Hill, Frédéric Blain, Marina Fomicheva, Lucia Specia, Lisa Yankovskaya

Translating text into a language unknown to the text's author, dubbed outbound translation, is a modern need for which the user experience has significant room for improvement, beyond the basic machine translation facility.

Machine Translation Translation

Unsupervised Quality Estimation for Neural Machine Translation

3 code implementations21 May 2020 Marina Fomicheva, Shuo Sun, Lisa Yankovskaya, Frédéric Blain, Francisco Guzmán, Mark Fishel, Nikolaos Aletras, Vishrav Chaudhary, Lucia Specia

Quality Estimation (QE) is an important component in making Machine Translation (MT) useful in real-world applications, as it is aimed to inform the user on the quality of the MT output at test time.

Machine Translation Translation

Findings of the WMT 2019 Shared Tasks on Quality Estimation

no code implementations WS 2019 Erick Fonseca, Lisa Yankovskaya, Andr{\'e} F. T. Martins, Mark Fishel, Christian Federmann

We report the results of the WMT19 shared task on Quality Estimation, i. e. the task of predicting the quality of the output of machine translation systems given just the source text and the hypothesis translations.

Machine Translation Translation

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