Onception: Active Learning with Expert Advice for Real World Machine Translation. (arXiv:2203.04507v1 [cs.CL])

Active learning can play an important role in low-resource settings (i.e.,
where annotated data is scarce), by selecting which instances may be more
worthy to annotate. Most active learning approaches for Machine Translation
assume the existence of a pool of sentences in a source language, and rely on
human annotators to provide translations or post-edits, which can still be
costly. In this paper, we assume a real world human-in-the-loop scenario in
which: (i) the source sentences may not be readily …

active learning advice arxiv learning machine machine translation translation