  While Active Learning (AL) techniques are explored in Neural Machine
Translation (NMT), only a few works focus on tackling low annotation budgets
where a limited number of sentences can get translated. Such situations are
especially challenging and can occur for endangered languages with few human
annotators or having cost constraints to label large amounts of data. Although
AL is shown to be helpful with large budgets, it is not enough to build
high-quality translation systems in these low-resource conditions. In this
work, we propose a cost-effective training procedure to increase the
performance of NMT models utilizing a small number of annotated sentences and
dictionary entries. Our method leverages monolingual data with self-supervised
objectives and a small-scale, inexpensive dictionary for additional supervision
to initialize the NMT model before applying AL. We show that improving the
model using a combination of these knowledge sources is essential to exploit AL
strategies and increase gains in low-resource conditions. We also present a
novel AL strategy inspired by domain adaptation for NMT and show that it is
effective for low budgets. We propose a new hybrid data-driven approach, which
samples sentences that are diverse from the labelled data and also most similar
to unlabelled data. Finally, we show that initializing the NMT model and
further using our AL strategy can achieve gains of up to $13$ BLEU compared to
conventional AL methods.
