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In this article, we present a sampling-based approach to improve bilingual sub-sentential alignment in parallel corpora. This approach can be used to align parallel sentences on an as needed basis, and is able to accurately align newly available sentences. We evaluate the resulting alignments on several Machine Translation tasks. Results show that for the tasks considered here, our approach performs on par with the state-of-the-art statistical alignment pipeline giza++/Moses, and obtains superior results in a number of configurations, notably when aligning additional parallel sentence pairs carefully selected to match the test input.