  Annotation projection is an important area in NLP that can greatly contribute
to creating language resources for low-resource languages. Word alignment plays
a key role in this setting. However, most of the existing word alignment
methods are designed for a high resource setting in machine translation where
millions of parallel sentences are available. This amount reduces to a few
thousands of sentences when dealing with low-resource languages failing the
existing established IBM models. In this paper, we propose subword
sampling-based alignment of text units. This method's hypothesis is that the
aggregation of different granularities of text for certain language pairs can
help word-level alignment. For certain languages for which gold-standard
alignments exist, we propose an iterative Bayesian optimization framework to
optimize selecting possible subwords from the space of possible subword
representations of the source and target sentences. We show that the subword
sampling method consistently outperforms word-level alignment on six language
pairs: English-German, English-French, English-Romanian, English-Persian,
English-Hindi, and English-Inuktitut. In addition, we show that the
hyperparameters learned for certain language pairs can be applied to other
languages at no supervision and consistently improve the alignment results. We
observe that using $5K$ parallel sentences together with our proposed subword
sampling approach, we obtain similar F1 scores to the use of $100K$'s of
parallel sentences in existing word-level fast-align/eflomal alignment methods.
