  We present a simple neural network for word alignment that builds source and
target word window representations to compute alignment scores for sentence
pairs. To enable unsupervised training, we use an aggregation operation that
summarizes the alignment scores for a given target word. A soft-margin
objective increases scores for true target words while decreasing scores for
target words that are not present. Compared to the popular Fast Align model,
our approach improves alignment accuracy by 7 AER on English-Czech, by 6 AER on
Romanian-English and by 1.7 AER on English-French alignment.
