APE-then-QE: Correcting then Filtering Pseudo Parallel Corpora for MT
  Training Data Creation
Akshay Batheja Sourabh Deoghare Diptesh Kanojia Pushpak Bhattacharyya
Automatic Post-Editing (APE) is the task of automatically identifying and
correcting errors in the Machine Translation (MT) outputs. We propose a
repair-filter-use methodology that uses an APE system to correct errors on the
target side of the MT training data. We select the sentence pairs from the
original and corrected sentence pairs based on the quality scores computed
using a Quality Estimation (QE) model. To the best of our knowledge, this is a
novel adaptation of APE and QE to extract quality parallel corpus from the
pseudo-parallel corpus. By training with this filtered corpus, we observe an
improvement in the Machine Translation system's performance by 5.64 and 9.91
BLEU points, for English-Marathi and Marathi-English, over the baseline model.
The baseline model is the one that is trained on the whole pseudo-parallel
corpus. Our work is not limited by the characteristics of English or Marathi
languages; and is language pair-agnostic, given the necessary QE and APE data.