Learning to actively learn neural machine translation

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Traditional active learning (AL) methods for machine translation (MT) rely on heuristics. However, these heuristics are limited when the characteristics of the MT problem change due to e.g. the language pair or the amount of the initial bitext. In this paper, we present a framework to learn sentence selection strategies for neural MT. We train the AL query strategy using a high-resource language-pair based on AL simulations, and then transfer it to the low-resource language-pair of interest. The learned query strategy capitalizes on the shared characteristics between the language pairs to make an effective use of the AL budget. Our experiments on three language-pairs confirms that our method is more effective than strong heuristic-based methods in various conditions, including cold-start and warm-start as well as small and extremely small data conditions.

Original languageEnglish
Title of host publicationCoNLL 2018 - The 22nd Conference on Computational Natural Language Learning - Proceedings of the Conference
EditorsMiikka Silfverberg
Place of PublicationStroudsburg PA USA
PublisherAssociation for Computational Linguistics (ACL)
Number of pages11
ISBN (Electronic)9781948087728
Publication statusPublished - 2018
EventConference on Natural Language Learning 2018 - Brussels, Belgium
Duration: 31 Oct 20181 Nov 2018
Conference number: 22nd
https://www.aclweb.org/anthology/volumes/K18-1/ (Proceedings)

Publication series

NameCoNLL 2018 - 22nd Conference on Computational Natural Language Learning, Proceedings


ConferenceConference on Natural Language Learning 2018
Abbreviated titleCoNLL 2018
Internet address

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