  Quality Estimation (QE) is the task of evaluating the quality of a
translation when reference translation is not available. The goal of QE aligns
with the task of corpus filtering, where we assign the quality score to the
sentence pairs present in the pseudo-parallel corpus. We propose a Quality
Estimation based Filtering approach to extract high-quality parallel data from
the pseudo-parallel corpus. To the best of our knowledge, this is a novel
adaptation of the QE framework to extract quality parallel corpus from the
pseudo-parallel corpus. By training with this filtered corpus, we observe an
improvement in the Machine Translation (MT) system's performance by up to 1.8
BLEU points, for English-Marathi, Chinese-English, and Hindi-Bengali language
pairs, over the baseline model. The baseline model is the one that is trained
on the whole pseudo-parallel corpus. Our Few-shot QE model transfer learned
from the English-Marathi QE model and fine-tuned on only 500 Hindi-Bengali
training instances, shows an improvement of up to 0.6 BLEU points for
Hindi-Bengali language pair, compared to the baseline model. This demonstrates
the promise of transfer learning in the setting under discussion. QE systems
typically require in the order of (7K-25K) of training data. Our Hindi-Bengali
QE is trained on only 500 instances of training that is 1/40th of the normal
requirement and achieves comparable performance. All the scripts and datasets
utilized in this study will be publicly available.
