  We introduce a novel discriminative word alignment model, which we integrate
into a Transformer-based machine translation model. In experiments based on a
small number of labeled examples (~1.7K-5K sentences) we evaluate its
performance intrinsically on both English-Chinese and English-Arabic alignment,
where we achieve major improvements over unsupervised baselines (11-27 F1). We
evaluate the model extrinsically on data projection for Chinese NER, showing
that our alignments lead to higher performance when used to project NER tags
from English to Chinese. Finally, we perform an ablation analysis and an
annotation experiment that jointly support the utility and feasibility of
future manual alignment elicitation.
