A User Study of the Incremental Learning in NMT

Miguel Domingo, Mercedes García-Martínez, Álvaro Peris, Alexandre Helle, Amando Estela, Laurent Bié, Francisco Casacuberta, Manuel Herranz


Abstract
In the translation industry, human experts usually supervise and post-edit machine translation hypotheses. Adaptive neural machine translation systems, able to incrementally update the underlying models under an online learning regime, have been proven to be useful to improve the efficiency of this workflow. However, this incremental adaptation is somewhat unstable, and it may lead to undesirable side effects. One of them is the sporadic appearance of made-up words, as a byproduct of an erroneous application of subword segmentation techniques. In this work, we extend previous studies on on-the-fly adaptation of neural machine translation systems. We perform a user study involving professional, experienced post-editors, delving deeper on the aforementioned problems. Results show that adaptive systems were able to learn how to generate the correct translation for task-specific terms, resulting in an improvement of the user’s productivity. We also observed a close similitude, in terms of morphology, between made-up words and the words that were expected.
Anthology ID:
2020.eamt-1.34
Volume:
Proceedings of the 22nd Annual Conference of the European Association for Machine Translation
Month:
November
Year:
2020
Address:
Lisboa, Portugal
Venue:
EAMT
SIG:
Publisher:
European Association for Machine Translation
Note:
Pages:
319–328
Language:
URL:
https://aclanthology.org/2020.eamt-1.34
DOI:
Bibkey:
Cite (ACL):
Miguel Domingo, Mercedes García-Martínez, Álvaro Peris, Alexandre Helle, Amando Estela, Laurent Bié, Francisco Casacuberta, and Manuel Herranz. 2020. A User Study of the Incremental Learning in NMT. In Proceedings of the 22nd Annual Conference of the European Association for Machine Translation, pages 319–328, Lisboa, Portugal. European Association for Machine Translation.
Cite (Informal):
A User Study of the Incremental Learning in NMT (Domingo et al., EAMT 2020)
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PDF:
https://aclanthology.org/2020.eamt-1.34.pdf