1 code implementation • LREC 2022 • Marina Fomicheva, Shuo Sun, Erick Fonseca, Chrysoula Zerva, Frédéric Blain, Vishrav Chaudhary, Francisco Guzmán, Nina Lopatina, Lucia Specia, André F. T. Martins
We present MLQE-PE, a new dataset for Machine Translation (MT) Quality Estimation (QE) and Automatic Post-Editing (APE).
no code implementations • 3 Sep 2020 • Michael Lomnitz, Zigfried Hampel-Arias, Nina Lopatina, Felipe A. Mejia
Employing machine learning models in the real world requires collecting large amounts of data, which is both time consuming and costly to collect.
no code implementations • 31 Oct 2019 • Michael Lomnitz, Nina Lopatina, Paul Gamble, Zigfried Hampel-Arias, Lucas Tindall, Felipe A. Mejia, Maria Alejandra Barrios
It is critical to understand the privacy and robustness vulnerabilities of machine learning models, as their implementation expands in scope.
no code implementations • 15 Jun 2019 • Felipe A. Mejia, Paul Gamble, Zigfried Hampel-Arias, Michael Lomnitz, Nina Lopatina, Lucas Tindall, Maria Alejandra Barrios
Adversarial training was introduced as a way to improve the robustness of deep learning models to adversarial attacks.