  Active learning (AL) techniques reduce labeling costs for training neural
machine translation (NMT) models by selecting smaller representative subsets
from unlabeled data for annotation. Diversity sampling techniques select
heterogeneous instances, while uncertainty sampling methods select instances
with the highest model uncertainty. Both approaches have limitations -
diversity methods may extract varied but trivial examples, while uncertainty
sampling can yield repetitive, uninformative instances. To bridge this gap, we
propose HUDS, a hybrid AL strategy for domain adaptation in NMT that combines
uncertainty and diversity for sentence selection. HUDS computes uncertainty
scores for unlabeled sentences and subsequently stratifies them. It then
clusters sentence embeddings within each stratum using k-MEANS and computes
diversity scores by distance to the centroid. A weighted hybrid score that
combines uncertainty and diversity is then used to select the top instances for
annotation in each AL iteration. Experiments on multi-domain German-English
datasets demonstrate the better performance of HUDS over other strong AL
baselines. We analyze the sentence selection with HUDS and show that it
prioritizes diverse instances having high model uncertainty for annotation in
early AL iterations.
