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Onception: Active Learning with Expert Advice for Real World Machine Translation. (arXiv:2203.04507v1 [cs.CL])
March 10, 2022, 2:11 a.m. | Vânia Mendonça (1 and 2), Ricardo Rei (1 and 2 and 3), Luisa Coheur (1 and 2), Alberto Sardinha (1 and 2) ((1) INESC-ID Lisboa, (2) Institut
cs.CL updates on arXiv.org arxiv.org
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
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