Contrary to previous years’ editions, this year we acquired our own human ratings based on expert-based human evaluation via Multidimensional Quality Metrics (MQM).
In this paper, we present the joint contribution of Unbabel and IST to the WMT 2021 Metrics Shared Task.
We present the joint contribution of IST and Unbabel to the WMT 2021 Shared Task on Quality Estimation.
Despite the progress in machine translation quality estimation and evaluation in the last years, decoding in neural machine translation (NMT) is mostly oblivious to this and centers around finding the most probable translation according to the model (MAP decoding), approximated with beam search.
Moreover, since we not know in advance which query strategy will be the most adequate for a certain language pair and set of Machine Translation models, we propose to dynamically combine multiple strategies using prediction with expert advice.
We present MT-Telescope, a visualization platform designed to facilitate comparative analysis of the output quality of two Machine Translation (MT) systems.
In Machine Translation, assessing the quality of a large amount of automatic translations can be challenging.
Overall, our systems achieve strong results for all language pairs on previous test sets and in many cases set a new state-of-the-art.
We present COMET, a neural framework for training multilingual machine translation evaluation models which obtains new state-of-the-art levels of correlation with human judgements.
1 code implementation • 27 Aug 2020 • Jose David Bermudez Castro, Ricardo Rei, Jose E. Ruiz, Pedro Achanccaray Diaz, Smith Arauco Canchumuni, Cristian Muñoz Villalobos, Felipe Borges Coelho, Leonardo Forero Mendoza, Marco Aurelio C. Pacheco
This work provides a fast detection system of COVID-19 characteristics in X-Ray images based on deep learning (DL) techniques.