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Active learning can play an important role in low-resource settings (i.e.,where annotated data is scarce), by selecting which instances may be moreworthy to annotate. Most active learning approaches for Machine Translationassume the existence of a pool of sentences in a source language, and rely onhuman annotators to provide translations or post-edits, which can still becostly. In this article, we assume a real world human-in-the-loop scenario inwhich: (i) the source sentences may not be readily available, but insteadarrive in a stream; (ii) the automatic translations receive feedback in theform of a rating, instead of a correct/edited translation, since thehuman-in-the-loop might be a user looking for a translation, but not be able toprovide one. To tackle the challenge of deciding whether each incoming pairsource-translations is worthy to query for human feedback, we resort to anumber of stream-based active learning query strategies. Moreover, since we notknow in advance which query strategy will be the most adequate for a certainlanguage pair and set of Machine Translation models, we propose to dynamicallycombine multiple strategies using prediction with expert advice. Ourexperiments show that using active learning allows to converge to the bestMachine Translation systems with fewer human interactions. Furthermore,combining multiple strategies using prediction with expert advice oftenoutperforms several individual active learning strategies with even fewerinteractions.

Onception: Active Learning with Expert Advice for Real World Machine Translation