no code implementations • WMT (EMNLP) 2021 • Chrysoula Zerva, Daan van Stigt, Ricardo Rei, Ana C Farinha, Pedro Ramos, José G. C. de Souza, Taisiya Glushkova, Miguel Vera, Fabio Kepler, André F. T. Martins
We present the joint contribution of IST and Unbabel to the WMT 2021 Shared Task on Quality Estimation.
1 code implementation • WMT (EMNLP) 2021 • Ricardo Rei, Ana C Farinha, Chrysoula Zerva, Daan van Stigt, Craig Stewart, Pedro Ramos, Taisiya Glushkova, André F. T. Martins, Alon Lavie
In this paper, we present the joint contribution of Unbabel and IST to the WMT 2021 Metrics Shared Task.
1 code implementation • 30 May 2023 • Taisiya Glushkova, Chrysoula Zerva, André F. T. Martins
Although neural-based machine translation evaluation metrics, such as COMET or BLEURT, have achieved strong correlations with human judgements, they are sometimes unreliable in detecting certain phenomena that can be considered as critical errors, such as deviations in entities and numbers.
1 code implementation • 13 Sep 2022 • Ricardo Rei, Marcos Treviso, Nuno M. Guerreiro, Chrysoula Zerva, Ana C. Farinha, Christine Maroti, José G. C. de Souza, Taisiya Glushkova, Duarte M. Alves, Alon Lavie, Luisa Coheur, André F. T. Martins
We present the joint contribution of IST and Unbabel to the WMT 2022 Shared Task on Quality Estimation (QE).
1 code implementation • 13 Apr 2022 • Chrysoula Zerva, Taisiya Glushkova, Ricardo Rei, André F. T. Martins
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.
2 code implementations • Findings (EMNLP) 2021 • Taisiya Glushkova, Chrysoula Zerva, Ricardo Rei, André F. T. Martins
Several neural-based metrics have been recently proposed to evaluate machine translation quality.
no code implementations • 8 Nov 2019 • Taisiya Glushkova, Ekaterina Artemova
We explore the abilities of character recurrent neural network (char-RNN) for hashtag segmentation.