  The most common tools for word-alignment rely on a large amount of parallel
sentences, which are then usually processed according to one of the IBM model
algorithms. The training data is, however, the same as for machine translation
(MT) systems, especially for neural MT (NMT), which itself is able to produce
word-alignments using the trained attention heads. This is convenient because
word-alignment is theoretically a viable byproduct of any attention-based NMT,
which is also able to provide decoder scores for a translated sentence pair.
  We summarize different approaches on how word-alignment can be extracted from
alignment scores and then explore ways in which scores can be extracted from
NMT, focusing on inferring the word-alignment scores based on output sentence
and token probabilities. We compare this to the extraction of alignment scores
from attention. We conclude with aggregating all of the sources of alignment
scores into a simple feed-forward network which achieves the best results when
combined alignment extractors are used.
