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.
no code implementations • WMT (EMNLP) 2021 • Lisa Yankovskaya, Mark Fishel
The paper presents our submission to the WMT2021 Shared Task on Quality Estimation (QE).
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.
3 code implementations • 21 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.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Rachel Bawden, Biao Zhang, Lisa Yankovskaya, Andre Tättar, Matt Post
We investigate a long-perceived shortcoming in the typical use of BLEU: its reliance on a single reference.
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.