AQET (Adaptive Quality Estimation Tool) is an open-source package for performing Quality Estimation for Machine Translation, i.e. for determining the quality of an automatic translation given its source sentence and without recourse to reference translations.
AQET is able to continuously learn from post-edited sentences, targeting reactivity and robustness to user and domain changes.
AQET has been developed to support professional translators during their daily work and it is suitable for being embedded in a Computer-Assisted Translation tool.
The current version (v1.0) supports two online machine learning algorithms: Online Support Vector Regression (Online SVR) and Passive-Aggressive.
AQET takes advantage of third-party open-source software:
- QuEst: an open source tool for translation quality estimation
- Online SVR: C++ implementation of Online Support Vector Regression algorithm
- sofia-ml: suite of fast incremental algorithms for machine learning
- tercpp: C++ implementation of TER metric
The development of AQET was partially supported by the EC-funded project MateCat (ICT-2011.4.2-287688).
AQET is distributed under the GNU General Public License version 3 (GPLv3).
Installation, configuration and usage instructions are available here.
Source code is available here.
If you intend to use AQET please cite:
Turchi, Marco, Antonios Anastasopoulos, José G. C. de Souza, and Matteo Negri. 2014. “Adaptive Quality Estimation for Machine Translation”. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Baltimore, Maryland, pp. 710–720.
For questions and support about AQET please contact: turchi [at] fbk [dot] eu