OpenKiwi: An Open Source Framework for Quality Estimation
This provides a tool for researchers and practitioners in machine translation to estimate translation quality, but it is incremental as it implements existing winning systems.
The authors tackled the problem of translation quality estimation by introducing OpenKiwi, an open source PyTorch-based framework that supports training and testing for word-level and sentence-level systems, achieving state-of-the-art performance on word-level tasks and near state-of-the-art on sentence-level tasks in WMT 2018 benchmarks.
We introduce OpenKiwi, a PyTorch-based open source framework for translation quality estimation. OpenKiwi supports training and testing of word-level and sentence-level quality estimation systems, implementing the winning systems of the WMT 2015-18 quality estimation campaigns. We benchmark OpenKiwi on two datasets from WMT 2018 (English-German SMT and NMT), yielding state-of-the-art performance on the word-level tasks and near state-of-the-art in the sentence-level tasks.