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Computer Science > Computation and Language

arXiv:2310.14262 (cs)
[Submitted on 22 Oct 2023]

Title:Boosting Unsupervised Machine Translation with Pseudo-Parallel Data

Authors:Ivana Kvapilíková, Ondřej Bojar
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Abstract:Even with the latest developments in deep learning and large-scale language modeling, the task of machine translation (MT) of low-resource languages remains a challenge. Neural MT systems can be trained in an unsupervised way without any translation resources but the quality lags behind, especially in truly low-resource conditions. We propose a training strategy that relies on pseudo-parallel sentence pairs mined from monolingual corpora in addition to synthetic sentence pairs back-translated from monolingual corpora. We experiment with different training schedules and reach an improvement of up to 14.5 BLEU points (English to Ukrainian) over a baseline trained on back-translated data only.
Comments: MT Summit 2023
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2310.14262 [cs.CL]
  (or arXiv:2310.14262v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2310.14262
Journal reference: Ivana Kvapilíková, Ondřej Bojar (2023): Boosting Unsupervised Machine Translation with Pseudo-Parallel Data. In: Proceedings of Machine Translation Summit XIX vol. 1: Research Track, pp. 135-147, AAMT, Kyoto, Japan

Submission history

From: Ivana Kvapilikova [view email]
[v1] Sun, 22 Oct 2023 10:57:12 UTC (533 KB)
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