  After a period of decrease, interest in word alignments is increasing again
for their usefulness in domains such as typological research, cross-lingual
annotation projection, and machine translation. Generally, alignment algorithms
only use bitext and do not make use of the fact that many parallel corpora are
multiparallel. Here, we compute high-quality word alignments between multiple
language pairs by considering all language pairs together. First, we create a
multiparallel word alignment graph, joining all bilingual word alignment pairs
in one graph. Next, we use graph neural networks (GNNs) to exploit the graph
structure. Our GNN approach (i) utilizes information about the meaning,
position, and language of the input words, (ii) incorporates information from
multiple parallel sentences, (iii) adds and removes edges from the initial
alignments, and (iv) yields a prediction model that can generalize beyond the
training sentences. We show that community detection provides valuable
information for multiparallel word alignment. Our method outperforms previous
work on three word-alignment datasets and on a downstream task.
