Efficient Machine Translation Corpus Generation
Kamer Ali Yuksel Ahmet Gunduz Shreyas Sharma Hassan Sawaf
This paper proposes an efficient and semi-automated method for
human-in-the-loop post-editing for machine translation (MT) corpus generation.
The method is based on online training of a custom MT quality estimation metric
on-the-fly as linguists perform post-edits. The online estimator is used to
prioritize worse hypotheses for post-editing, and auto-close best hypotheses
without post-editing. This way, significant improvements can be achieved in the
resulting quality of post-edits at a lower cost due to reduced human
involvement. The trained estimator can also provide an online sanity check
mechanism for post-edits and remove the need for additional linguists to review
them or work on the same hypotheses. In this paper, the effect of prioritizing
with the proposed method on the resulting MT corpus quality is presented versus
scheduling hypotheses randomly. As demonstrated by experiments, the proposed
method improves the lifecycle of MT models by focusing the linguist effort on
production samples and hypotheses, which matter most for expanding MT corpora
to be used for re-training them.