  AM dependency parsing is a method for neural semantic graph parsing that
exploits the principle of compositionality. While AM dependency parsers have
been shown to be fast and accurate across several graphbanks, they require
explicit annotations of the compositional tree structures for training. In the
past, these were obtained using complex graphbank-specific heuristics written
by experts. Here we show how they can instead be trained directly on the graphs
with a neural latent-variable model, drastically reducing the amount and
complexity of manual heuristics. We demonstrate that our model picks up on
several linguistic phenomena on its own and achieves comparable accuracy to
supervised training, greatly facilitating the use of AM dependency parsing for
new sembanks.
